WO2023207822A1 - 通信方法、设备和存储介质 - Google Patents

通信方法、设备和存储介质 Download PDF

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Publication number
WO2023207822A1
WO2023207822A1 PCT/CN2023/089989 CN2023089989W WO2023207822A1 WO 2023207822 A1 WO2023207822 A1 WO 2023207822A1 CN 2023089989 W CN2023089989 W CN 2023089989W WO 2023207822 A1 WO2023207822 A1 WO 2023207822A1
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Prior art keywords
target
receiving
transmit
transmitting
receive
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PCT/CN2023/089989
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English (en)
French (fr)
Inventor
鲁照华
肖华华
刘文丰
王瑜新
郑国增
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中兴通讯股份有限公司
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Publication of WO2023207822A1 publication Critical patent/WO2023207822A1/zh

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0617—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal for beam forming
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0619—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal using feedback from receiving side
    • H04B7/0621—Feedback content
    • H04B7/0626—Channel coefficients, e.g. channel state information [CSI]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
    • H04B7/0837—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station using pre-detection combining
    • H04B7/0842—Weighted combining
    • H04B7/086—Weighted combining using weights depending on external parameters, e.g. direction of arrival [DOA], predetermined weights or beamforming

Definitions

  • the present application relates to the field of communication, for example, to a communication method, device and storage medium.
  • AI artificial intelligence
  • ML machine learning
  • the research content includes but is not limited to channel state information feedback, beam management, channel estimation, positioning, interference management, user scheduling, etc.
  • beam management including but not limited to beam training, beam tracking, and beam recovery, several aspects need to be solved.
  • the core issue is how to obtain accurate beam pairs with the lowest possible control overhead.
  • beam forming is mainly used to concentrate the transmission energy in the direction of the user to obtain gain.
  • N transmitting beams at the transmitting end and M receiving beams at the receiving end it takes up to N*M beam scanning to select the optimal beam. If scanning is performed in stages, such as fixing a receiving beam and scanning different N transmit beams are selected to select the optimal transmit beam, and then based on the optimal transmit beam, the transmit beam is fixed and M different receive beams are scanned to select the optimal receive beam. Therefore, a better transceiver beam pair can be obtained through at least N+M beam scanning. However, as the carrier frequency further increases, the number of beams will further increase.
  • One method is to use artificial intelligence to predict the optimal value among N transmit beams using N1 transmit beams and/or predict the optimal value among M receive beams using M1 receive beams. Among them N1 ⁇ N, M1 ⁇ M. However, since the predicted transmitting beam is not actually transmitted, the receiving end cannot determine what the corresponding preferred receiving beam is.
  • the embodiment of this application provides a communication method, which is applied to the sending end and includes:
  • the target transmit beam is a transmit beam determined according to a preset transmit beam set, wherein the preset transmit beam set includes a first transmit beam set and a second transmit beam set ;
  • Send target reception beam indication signaling which is used to instruct the receiving end to determine the target reception beam, and use the determined target reception beam for information transmission.
  • the embodiment of this application provides a communication method, which is applied to the receiving end and includes:
  • the target receiving beam perform information transmission according to the acquired target receiving beam; the target receiving beam corresponds to the target transmitting beam, and the target transmitting beam is a transmitting beam determined according to a preset transmitting beam set, wherein the preset The transmit beam set includes a first transmit beam set and a second transmit beam set.
  • Embodiments of the present application provide a communication device, including: a memory, and one or more processors;
  • the memory is configured to store one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any of the above embodiments. method.
  • Embodiments of the present application provide a storage medium that stores a computer program.
  • the computer program is executed by a processor, the communication method described in any of the above embodiments is implemented.
  • Figure 1 is a flow chart of a communication method provided by an embodiment of the present application.
  • Figure 2 is a flow chart of another communication method provided by an embodiment of the present application.
  • Figure 3 is a structural block diagram of a communication device provided by an embodiment of the present application.
  • Figure 4 is a structural block diagram of another communication device provided by an embodiment of the present application.
  • Figure 5 is a schematic structural diagram of a communication device provided by an embodiment of the present application.
  • the network architecture of the mobile communication network may include network side devices (such as Including but not limited to base stations) and receiving side equipment (for example, including but not limited to terminals).
  • the first communication node may also be referred to as the first communication node device
  • the second communication node may also be referred to as the second communication node device
  • the first communication node may be a terminal side device
  • the second communication node may be a base station side device.
  • both the first communication node and the second communication node may be a base station or a terminal.
  • the base station may be an evolutionary base station (Evolutional Node B, eNB or eNodeB) in Long Term Evolution (LTE), Long Term Evolution Advanced (LTEA), or a 5G network.
  • Base station equipment or future communication Base stations in communication systems, etc.
  • Base stations can include various macro base stations, micro base stations, home base stations, wireless remotes, routers, wireless fidelity (Wireless Fidelity, WIFI) equipment or primary cells and secondary cells. ) and other network-side devices, location management function (LMF) equipment.
  • LMF location management function
  • the terminal is a device with wireless transceiver function. It can be deployed on land, including indoors or outdoors, handheld, wearable or vehicle-mounted; it can also be deployed on water (such as ships, etc.); it can also be deployed in the air (such as aircraft, balloons, satellites, etc.).
  • the terminal can be a mobile phone (mobile phone), tablet computer (Pad), computer with wireless transceiver function, virtual reality (Virtual Reality, VR) terminal, augmented reality (Augmented Reality, AR) terminal, or industrial control (Industrial Control) Wireless terminals, wireless terminals in self-driving, wireless terminals in remote medical, wireless terminals in smart grid, wireless terminals in transportation safety, smart cities Wireless terminals in smart city, wireless terminals in smart home, etc.
  • the embodiments of this application do not limit application scenarios.
  • the terminal can sometimes also be called a user, User Equipment (UE), access terminal, UE unit, UE station, mobile station, mobile station, remote station, remote terminal, mobile device, UE terminal, wireless communication equipment, UE Agent or UE device, etc.
  • UE User Equipment
  • high-level signaling includes but is not limited to Radio Resource Control (RRC) and Media Access Control-Control Element (MAC CE); the base station and the terminal can also Transmit physical layer signaling.
  • RRC Radio Resource Control
  • MAC CE Media Access Control-Control Element
  • the base station and the terminal can also Transmit physical layer signaling.
  • the downlink transmits physical layer signaling on the Physical Downlink Control Channel (Physical Downlink Control CHannel, PDCCH), and the uplink transmits the physical layer on the Physical Uplink Control Channel (Physical Uplink Control CHannel, PUCCH).
  • Signaling Physical random access channel (Physical random-access channel, PRACH).
  • the indicators of various parameters may also be called indexes (Index) or identifiers (Identifier, ID), which are completely equivalent concepts.
  • the resource identifier of the wireless system include but are not limited to one of the following: a reference signal resource, a reference signal resource group, a reference signal resource configuration, a channel state information (CSI) report, a CSI report set, Corresponding indexes for terminals, base stations, panels, neural networks, sub-neural networks, neural network layers, etc.
  • the base station may indicate the identity of one or a group of resources to the terminal through various high-layer signaling or physical layer signaling.
  • a beam set may also be called a beam set.
  • Beams include transmit beams and/or receive beams.
  • a process of beam scanning may be: at least one transmitting beam transmits corresponding reference signal resources, at least one receiving beam receives the reference signal resources transmitted by the at least one transmitting beam, and calculates at least one received beam and a beam corresponding to at least one transmit beam Metric parameters, and determine the preferred K beam pairs according to the beam metric parameters.
  • a beam pair includes a transmit beam and a receive beam, and K is a positive integer.
  • the beam here can be replaced by an antenna or a port.
  • transmission includes sending or receiving. Such as sending or receiving data, sending or receiving signals.
  • the base station or user in order to calculate channel state information or perform channel estimation, mobility management, positioning, etc., the base station or user needs to send a reference signal (Reference Signal, RS).
  • the reference signal includes but is not limited to Positioning Reference Signal (Positioning Reference Signal). , PRS), Channel-State Information reference signal (CSI-RS), Synchronization Signals Block (SSB), Physical Broadcast Channel (PBCH), Synchronization Broadcast Block/Physical Broadcast Channel (SSB/PBCH).
  • CSI-RS includes zero power CSI-RS (Zero Power CSI-RS, ZP CSI-RS) and non-zero power CSI-RS (Non-Zero Power CSI-RS, NZP CSI-RS).
  • Channel status information Interference measurement signal (Channel-State Information-Interference Measurement, CSI-IM), Sounding Reference Signal (SRS), NZP CSI-RS can be used to measure the channel or interference
  • CSI-RS can also be used for tracking, It is called tracking reference signal (CSI-RS for Tracking, TRS)
  • CSI-IM is generally used to measure interference
  • SRS is used for uplink channel estimation and CSI measurement CSI-RS, SRS, CSI-IM and other reference signals.
  • SSB and/or PBCH may be collectively referred to as SSB.
  • the transmission also includes time domain characteristics, where the time domain characteristics include but are not limited to aperiodic, periodic, and semi-persistent characteristics, which respectively indicate that the transmitted reference signal is aperiodic transmission and periodic transmission. , or semi-continuous transmission.
  • periodic reference signals or semi-persistent reference signals will be configured with a period and/or slot offset (slot offset) information through high-level signaling.
  • slot offset slot offset
  • These two parameters can be jointly encoded (for example, through high-level signaling periodicity And Offset Configuration, by obtaining this parameter, the user can know the transmission period of the periodic or semi-persistent reference signal, and the transmission time slot).
  • the resources for transmitting reference signals can be called reference signal resources.
  • multiple reference signal resources can be divided into multiple sets (such as CSI-RS resource set, CSI-IM resource set, SRS resource set), the reference signal resource set includes at least one reference signal resource, and multiple reference signal resource sets can all come from the same reference signal resource setting (such as CSI-RS resource setting, SRS resource setting, CSI-IM resource setting, where CSI-IM resource setting may be merged with CSI-IM resource setting, both called CSI-RS resource setting) to configure parameter information.
  • the reference signal resource set includes at least one reference signal resource
  • multiple reference signal resource sets can all come from the same reference signal resource setting (such as CSI-RS resource setting, SRS resource setting, CSI-IM resource setting, where CSI-IM resource setting may be merged with CSI-IM resource setting, both called CSI-RS resource setting) to configure parameter information.
  • the base station configures measurement resource information, and the measurement resource information is used to obtain channel state information.
  • the measurement resource information includes at least one channel measurement resource (Channel Measurement Resource, CMR) information and at least one interference measurement resource (Interference Measurement Resource, IMR) information.
  • CMR Channel Measurement Resource
  • IMR Interference Measurement Resource
  • the base station has a report config or reporting setting. Configure measurement resource information in setting).
  • the CMR information is used to enable the terminal to measure the channel status of each beam
  • the IMR information is used to enable the terminal to measure the interference suffered by each beam.
  • artificial intelligence includes machine learning (ML), deep learning, reinforcement learning, transfer learning, deep reinforcement learning, meta-learning and other self-learning devices, components, software, module.
  • artificial intelligence is implemented through an artificial intelligence network (also known as a neural network).
  • the neural network includes multiple layers, and each layer includes at least one node.
  • the neural network includes an input layer, an output layer, and at least one hidden layer, wherein each layer of the neural network includes, but is not limited to, a fully connected layer, a dense layer, a convolutional layer, a transposed convolutional layer, and a direct connection. At least one of layers, activation functions, normalization layers, pooling layers, etc.
  • each layer of the neural network may include a sub-neural network, such as a residual block (Residual Network block, ResNet block), a dense network (DenseNet Block), a recurrent network (Recurrent Neural Network, RNN), etc.
  • the artificial intelligence network includes a neural network model and/or neural network parameters corresponding to the neural network model, where the neural network model may be referred to as a network model, and the neural network parameters may be referred to as network parameters.
  • a network model defines the number of layers of the neural network, the size of each layer, activation function, connection status, convolution kernel and convolution step size, convolution type (such as 1D convolution, 2D convolution, 3D convolution, hollow convolution, transposed convolution, separable convolution, grouped convolution, expanded convolution, etc.), and the network parameters are the weights and/or biases of each layer of the network in the network model and their values .
  • a network model can correspond to multiple sets of different neural network parameter values to adapt to different scenarios. The values of network parameters can be obtained through offline training and/or online training.
  • a neural network model can correspond to multiple different neural network parameter values.
  • Beam management includes but is not limited to beam scanning, beam tracking and beam recovery.
  • the core problem that needs to be solved is how to obtain accurate beam pairs with the lowest possible control overhead.
  • beam scanning includes transmitting end beam scanning and/or receiving end beam scanning.
  • two-stage scanning can be used.
  • beam training may include training of P1, P2, and P3.
  • the beam of the transmitting end and the beam of the receiving end are scanned at the same time; in the beam scanning of the P2 stage, a receiving beam is fixed and different transmitting beams are scanned; in the P3 stage, a transmitting beam is fixed and different receiving beams are scanned.
  • the repetition parameter repetition is set to off, and then measuring the RSRP corresponding to the N beams, finding the optimal L ones for reporting.
  • one beam is sent, M receiving beams are received, the repetition parameter is set to on, and then the RSRPs corresponding to the M beams are measured to find the optimal L ones and report them.
  • the beam prediction function of AI can be used, that is, only the beam metric parameters corresponding to L0 beams are input, but the beam metric parameters corresponding to L1 beams are predicted based on the L0 beam metric parameters.
  • the L1 beams may include the L0 beams, where L0 ⁇ L1, and they are all positive integers, and the beams may be transmitting beams, receiving beams, or transmitting and receiving beam pairs. Each beam can correspond to a beam number direction, where N, M, L, L1, and L0 are all positive integers.
  • the beams include transmit beams, receive beams, precoding, precoding matrices, precoding matrix indexes, receive beams and transmit beam pairs, transmit beams and receive beam pairs.
  • the beam can be a resource (such as originating precoding, receiving precoding, antenna port, antenna weight vector, antenna weight matrix, etc.), and the beam index can be replaced with a resource index, because the beam can be associated with some time-frequency resources. Binding on transport.
  • the beam can also be a transmission (sending/receiving) method; the transmission method can include spatial division multiplexing, frequency domain/time domain diversity, etc.
  • the receiving beam indication means that the transmitting end can feedback the reported reference signal resource (or reference signal resource, reference signal resource index) and antenna port to the UE through the current reference signal resource (or reference signal resource index) and antenna port.
  • a beam pair consists of a transmit beam and a receive beam.
  • the beam direction or beam angle may include at least one of the following: angle of arrival (Angle Of Arrival, AOA), angle of departure (Angle Of Departure, AOD), ZOD (Zenith angle Of Departure), ZOA (Zenith angle Of Arrival), vector or vector index constructed from at least one angle of AOA, AOD, ZOD, ZOA, Discrete Fourier Transformation (DFT) vector, codeword in the codebook, transmit beam index, receive beam index , transmit beam group index, receive beam group index.
  • the beam refers to a spatial filter or a spatial receiving/transmitting parameter.
  • the spatial filtering may be at least one of the following: DFT vector, precoding vector, DFT matrix, precoding matrix, or a linear combination of multiple DFTs. vector, a vector composed of a linear combination of multiple precoding vectors.
  • the AI device used to predict beam metric parameters is implemented through a neural network.
  • the beam metric parameters corresponding to L0 beams are combined into a beam metric parameter array (the first beam metric parameter array) and input into the neural network.
  • the neural network outputs the beam metric parameter array corresponding to L1 beams (the second beam metric parameter array), and passes The index corresponding to the largest beam metric parameter in the beam metric parameter array corresponding to the L1 beams determines the optimal beam.
  • L1 is generally greater than L0, and both are positive integers.
  • the parameters of the neural network are obtained through online training or offline training. For example, by inputting at least one sample and a label, the neural network model is trained to obtain neural network parameters.
  • the sample is a first beam metric parameter array measured by a terminal
  • the label is a second beam metric parameter array corresponding to the first beam metric parameter array measured by a terminal.
  • the first beam metric parameter array and the second beam metric parameter array have a corresponding relationship, for example, a one-to-one correspondence.
  • the network can be known Predict performance, and train a neural network based on the loss function of both.
  • the transmit beam and/or receive beam index is numbered in an agreed manner to form a beam index.
  • a beam index includes one of the following: transmit beam index, receive beam index, transmit and receive beam pair index.
  • a beam index corresponds to a beam direction, or a vector or matrix corresponding to the beam direction.
  • the terminal receives reference signals (such as CSI-RS, SSB, etc.) and measures the beam metric parameters corresponding to each beam, and sorts them according to the size of the beam index to obtain a beam metric parameter array.
  • reference signals such as CSI-RS, SSB, etc.
  • the first beam metric parameter array is a beam metric parameter array formed by the beam metric parameters corresponding to the first beam set
  • the second beam metric parameter array is a beam metric parameter array formed by the beam metric parameters corresponding to the second beam set.
  • the first beam set is a subset of the second beam set.
  • the so-called normalization refers to normalizing the values of elements in an array to a value in an interval greater than or equal to a and less than or equal to b.
  • normalization in one example, divide the elements in the array by the variance in the array elements to achieve normalization; in one example, divide the elements in the array by a fixed value (such as in all samples The maximum value of all elements) to achieve normalization; in one example, the elements in the array are divided by a statistical value (such as the statistical variance value of all elements in all samples) to achieve normalization.
  • index values such as beam index, CRI, SSBRI, etc.
  • normalization can be achieved through One-Hot Encoding.
  • the beam metric parameter array is a 2-dimensional array, such as a vector. In some examples, the beam metric parameter array is a two-dimensional array, such as a matrix. In some examples, the beam metric parameter array is an array larger than two dimensions, such as a tensor. Among them, vectors and matrices can also be regarded as a special case of tensors.
  • the beam metric parameter is the reference signal received power (L1-RSRP or RSRP) of layer 1 corresponding to at least one beam; in some embodiments, the beam metric parameter is the reference signal received power (L1-RSRP or RSRP) of at least one beam corresponding to The reference signal signal-to-interference noise ratio (L1Signal-to-Interference Noise Ratio, L1-SINR or SINR) of layer 1; in some embodiments, the beam measurement parameter is the reference signal received quality (Reference Signal Received Quality, corresponding to at least one beam) RSRQ); in some embodiments, the beam metric parameter is the beam angle corresponding to at least one beam (at least one of AOA, ZOA, AOD, ZOD, etc., sometimes also called horizontal arrival angle, vertical arrival angle, horizontal departure angle, respectively, vertical departure angle); in some embodiments, the beam metric parameter is The transmit beam index corresponding to at least one beam; in some embodiments, the beam metric parameter is the receive beam index corresponding to at least one beam; in some
  • the beam metric parameter is a combination of at least two of the following beam metric parameters corresponding to at least one beam: RSRP, RSRQ, SINR, beam angle, transmit beam index, receive beam index, beam pair index, CRI, SSBRI wait.
  • the beam metric parameter is a linear value of one of RSRP, RSRQ, and SINR.
  • the beam metric parameter is the logarithm value of one of RSRP, RSRQ, and SINR, or decibel value (DB).
  • the beam metric parameters are based on CSI-RS measurements. In some embodiments, the beam metric parameters are based on SSB measurements, and in some embodiments the beam metric parameters are based on SRS measurements.
  • Each beam pair can obtain corresponding beam metric parameters (such as RSRP) by receiving the reference signal corresponding to the beam.
  • RSRP beam metric parameters
  • the beam metric parameter such as RSRP
  • Nr, Nt, N, and M can be positive integers.
  • At least one beam among the K preferred beams is used for data or signal transmission.
  • the beams here include transmitting beams and/or receiving beams, where N, M, and K are all integers, and K ⁇ 1, M ⁇ K, and N>M.
  • RSRP can be replaced by other beam metric parameters, such as at least one of RSRQ, SINR, BDRPM, beam angle, etc.
  • the base station or the terminal can select M beams from N beams, the base station only sends reference signal resources corresponding to the M beams, and the terminal obtains the beam metric parameters by receiving the reference signal resources corresponding to the M beams, and Combined into a beam metric parameter array in a certain order, the wave
  • the beam metric parameter array is normalized and then input into the neural network to obtain a beam metric parameter array containing N elements.
  • the beam is the preferred beam. At least one preferred beam is used for data or signal transmission.
  • the beams here include transmitting beams and/or receiving beams, where N, M, and K are integers, and K is greater than or equal to 1, M is greater than or equal to K, and N is greater than M.
  • the base station or the terminal can select M beams from N beams, the base station only sends reference signal resources corresponding to the M beams, and the terminal obtains the beam metric parameters by receiving the reference signal resources corresponding to the M beams, and Combine it into a beam metric parameter array in a certain order, normalize the beam metric parameter array and input it into the neural network to obtain the probabilities corresponding to N beams, and select the K beams with the highest probability as the preferred beams .
  • At least one preferred beam is used for data or signal transmission.
  • the beams here include transmitting beams and/or receiving beams, where N, M, and K are integers, and K is greater than or equal to 1, M is greater than or equal to K, and N is greater than M.
  • the base station or the terminal can select M beams from N beams, the base station only sends reference signal resources corresponding to the M beams, and the terminal obtains the beam metric parameters by receiving the reference signal resources corresponding to the M beams, and
  • the beam metric parameter array is combined into a beam metric parameter array in a certain order.
  • the beam metric parameter array is normalized and then input into the neural network to directly output K preferred beams or beam indexes. At least one preferred beam is used for data or signal transmission.
  • the beams here include transmitting beams and/or receiving beams, where N, M, and K are integers, and K is greater than or equal to 1, M is greater than or equal to K, and N is greater than M.
  • the base station or the terminal can select M beams from N beams, the base station only sends reference signal resources corresponding to the M beams, and the terminal obtains the beam metric parameters by receiving the reference signal resources corresponding to the M beams, and Combined into a beam metric parameter array in a certain order, the beam metric parameter array is fed back.
  • the base station receives the beam metric parameter array, normalizes the beam metric parameter array and then inputs it into the neural network to obtain a Beam metric parameter array, select the beam corresponding to the K beam metric parameters with the largest beam metric parameters (such as RSRP, SINR, RSRQ) in the beam metric parameter array as the preferred beam.
  • At least one preferred beam is used for data or signal transmission.
  • the beams here include transmitting beams and/or receiving beams, where N, M, and K are integers, and K is greater than or equal to 1, M is greater than or equal to K, and N is greater than M.
  • the base station or the terminal can select M beams from N beams, the base station only sends reference signal resources corresponding to the M beams, and the terminal obtains the beam metric parameters by receiving the reference signal resources corresponding to the M beams, and Combined into a beam metric parameter array in a certain order, the beam metric parameter array is fed back.
  • the base station receives the beam metric parameter array, normalizes the beam metric parameter array and then inputs it into the neural network to obtain the corresponding N beams. probability, and select the K beams with the highest probability as the preferred beams. Use at least one preferred beam for data or signal transmission.
  • the beams here include transmitting beams and/or receiving beams, where N, M, and K are integers, and K is greater than or equal to 1, M is greater than or equal to K, and N is greater than M.
  • the base station or the terminal can select M beams from N beams, the base station only sends reference signal resources corresponding to the M beams, and the terminal obtains the beam metric parameters by receiving the reference signal resources corresponding to the M beams, and Combined into a beam metric parameter array in a certain order, the beam metric parameter array is fed back.
  • the base station receives the beam metric parameter array, normalizes the beam metric parameter array and then inputs it into the neural network, and directly outputs K preferred ones. Beam or beam index. At least one preferred beam is used for data or signal transmission.
  • the beams here include transmitting beams and/or receiving beams, where N, M, and K are integers, and K is greater than or equal to 1, M is greater than or equal to K, and N is greater than M.
  • the N beams correspond to D reference signal resources
  • the M beams correspond to E reference signal resources
  • the base station sends the E reference signal resources, where the E reference signal resources belong to D reference signals.
  • a subset of resources, the E reference signal resources belong to the same reference signal resource set.
  • the terminal receives the E reference signal resources, obtains M beam metric parameters, and combines them into a beam metric parameter array in a certain order.
  • M E*R
  • R is the number of actually used receiving beams
  • D is the number of transmitting beams.
  • M, N, D, E, R, R1 are positive integers and N>M, D>E.
  • N is a multiple of M, for example, N is a natural number such as 2, 3, 4, 5, 6, etc. of M.
  • N-M beams do not actually transmit signals, and their beam metric values are predicted. Therefore, for one of the transmitting beams, the corresponding optimal receiving beam is not known at the receiving end. In view of this, it is necessary to propose a solution that can determine the receive beam corresponding to the predicted transmit beam.
  • FIG. 1 is a flow chart of a communication method provided by an embodiment of the present application. This embodiment can be executed by the sending end. Among them, the sending end can be a network side device, such as a base station, etc. As shown in Figure 1, the communication method in this embodiment includes: S110-S120.
  • the target transmission beam is a transmission beam determined according to a preset transmission beam set, where the preset transmission beam set includes a first transmission beam set and a second transmission beam set.
  • the target receiving beam may be a preferred receiving beam corresponding to the target transmitting beam.
  • Each transmit beam in the first transmit beam set corresponds to at least one actually transmitted reference signal resource, and at least one transmit beam in the first transmit beam set actually transmits at a certain time; the second transmit beam set corresponds to zero
  • the reference signal resources, or the reference signal resources corresponding to the beams in the second transmit beam set, are not actually transmitted.
  • at least one transmission beam in the first transmission beam set is a reference signal resource for actual transmission
  • the second transmission beam set The measurement parameters of all transmit beams in the combination are predicted, and the beams in the second transmit beam set correspond to reference signal resources that have not been actually transmitted, then the default transmit beam set is the first transmit beam set and the second transmit beam Union of sets.
  • the target transmission beam is a transmission beam determined according to a preset transmission beam set, and the target transmission beam is one or more transmission beams selected from the first transmission beam set and/or the second transmission beam set. In one embodiment, when the target transmission beam is at least one transmission beam in the second transmission beam set, since all transmission beams in the second transmission beam set have not actually transmitted, it needs to be determined according to certain rules or methods.
  • the target receive beam corresponding to the target transmit beam is a transmission beam determined according to a preset transmission beam set, and the target transmission beam is one or more transmission beams selected from the first transmission beam set and/or the second transmission beam set.
  • the target reception beam indication signaling is used to instruct the receiving end to determine the target reception beam and use the determined target reception beam for information transmission.
  • Information transmission includes: information sending and information receiving.
  • information transmission may include: data transmission and/or signal transmission, that is, data may be sent or received, and signals may also be sent or received.
  • the sending end determines the target receiving beam corresponding to the target transmitting beam
  • the sending end sends a target receiving beam indication instruction to the receiving end
  • the target receiving beam indication signaling is used to instruct the receiving end to determine the target receiving beam.
  • the receiving end can still perform according to the target receiving beam corresponding to the target transmitting beam indicated by the transmitting end.
  • Information transfer may include: data transmission and/or signal transmission, that is, data may be sent or received, and signals may also be sent or received.
  • the target reception beam indication signaling is sent through high-layer signaling and/or physical layer signaling.
  • the transmitting end may send target receiving beam indication signaling to the receiving end through high-level signaling and/or physical layer signaling, and the target receiving beam indicating signaling is expressed in The receiving end is instructed to determine a target receiving beam, and the determined target receiving beam is used for information transmission.
  • the target transmit beam is a transmit beam determined based on a preset transmit beam set, including: determining the target transmit beam based on an agreed receive beam and a beam metric parameter corresponding to the preset transmit beam set.
  • the target transmit beam may be determined based on the agreed receive beam and beam metric parameters corresponding to all transmit beams in the preset transmit beam set.
  • Each transmit beam in the preset transmit beam and the agreed receive beam are formed into a beam pair to obtain multiple corresponding beam pairs (where the number of beam pairs is the same as the total number of transmit beams in the preset transmit beam set), Then each beam pair corresponds to a reference signal resource, the reference signal resource is transmitted, the terminal receives the reference signal resource, and the reference signal resource received by the terminal is measured to obtain the beam metric parameters of each beam pair.
  • the terminal determines preferred beam pairs based on beam metric parameters.
  • the terminal feeds back the beam metric parameters, the base station receives the beam metric parameters fed back by the terminal, and the base station determines the preferred beam pair according to the beam metric parameters.
  • the beam pair corresponding to the beam metric parameter with the largest beam metric parameter is regarded as the preferred beam pair, wherein the transmission beam corresponding to the preferred beam pair is the target transmission beam.
  • Send beams, and the receiving beam corresponding to the preferred beam pair is the target receiving beam.
  • the transmitting end may use a target transmitting beam to transmit data or signals
  • the receiving end may use a target receiving beam to receive data or signals.
  • the beam metric parameters corresponding to the preset transmit beam set are determined based on the agreed receive beam and the beam metric parameters corresponding to the first transmit beam set.
  • the agreed receiving beam refers to a fixed receiving beam selected in advance.
  • the preset transmission beam set refers to the union of the first transmission beam set and the second transmission beam set.
  • the beam metric parameters corresponding to the first transmit beam set refer to the beam metric parameters corresponding to multiple beam pairs formed by combining the reference signal transmitted by each transmit beam and the agreed receive beam in the first transmit beam set.
  • the first transmit beam set Each beam transmits its corresponding reference signal resource, the terminal receives the reference signal resource through the agreed receiving beam, and measures the reference signal resource received by the terminal to obtain the beam metric parameters of each beam pair.
  • the terminal inputs beam metric parameters corresponding to the agreed receive beam and the first transmit beam set into the neural network to obtain beam prediction information corresponding to the beam pair composed of the agreed receive beam and the preset transmit beam set, and based on the The beam prediction information determines preferred beam pairs.
  • the terminal feeds back the beam metric parameters corresponding to the agreed receive beam and the first transmit beam set, the base station receives the agreed receive beam and the beam metric parameters corresponding to the first transmit beam set, and the base station converts the agreed receive beam to the first transmit beam set.
  • the beam metric parameters corresponding to the receive beam and the first transmit beam set are input into the neural network to obtain the beam prediction information corresponding to the beam pair composed of the agreed receive beam and the preset transmit beam set, and the preferred beam pair is determined based on the beam prediction information .
  • the beam prediction information here may be beam metric parameters, a general introduction to beam correspondence, or a preferred beam index.
  • the beam pair corresponding to the beam metric parameter with the largest beam metric parameter is the preferred beam pair, wherein the transmitting beam corresponding to the preferred beam pair is the target transmitting beam, and the receiving beam corresponding to the preferred beam pair is the target receiving beam. beam.
  • the transmitting end may use a target transmitting beam to transmit data or signals
  • the receiving end may use a target receiving beam to receive data or signals.
  • determining the target receiving beam corresponding to the target transmitting beam includes: determining the agreed receiving beam as the target receiving beam, and the agreed receiving beam includes one of the following: the receiving beam with the lowest receiving beam index; the receiving beam index The highest receiving beam; the receiving beam with the largest corresponding beam metric parameter in the preset receiving beam set.
  • the transmitting end transmits data or signals according to the target transmitting beam, and the receiving end uses the agreed receiving beam to receive the data or signals transmitted by the transmitting end.
  • the agreed receiving beam may be configured by the network side (eg, base station).
  • the agreed receiving beam may be the receiving beam with the lowest agreed beam index; in one example, the agreed receiving beam may be the receiving beam with the highest agreed beam index; in one example, the agreed receiving beam may be is the receiving beam with the largest beam metric parameter. In some embodiments, the agreed receiving beam is a default beam or a beam determined by the terminal itself or the base station confirms and instructs the terminal according to channel conditions.
  • determining the target receive beam corresponding to the target transmit beam includes: determining the target receive beam according to a preset receive beam set and the agreed beam metric parameters corresponding to the transmit beam.
  • the agreed transmission beam refers to a fixed transmission beam selected in advance.
  • the preset receiving beam set refers to the union of the first receiving beam set and the second receiving beam set.
  • the first receiving beam set includes beams that have received a reference signal resource corresponding to one transmit beam at least at one time; all the receive beams in the second receiving beam set have not received any reference signal resource corresponding to a transmit beam.
  • Each receive beam in the preset receive beam set and the agreed transmit beam are formed into a beam pair, and then multiple corresponding beam pairs are obtained (where the number of beam pairs is the same as the total number of receive beams in the preset receive beam set ), the agreed transmit beam transmits its corresponding reference signal resources in multiple time slots, the terminal receives the reference signal resources in multiple time slots through each receiving beam in the first receiving beam set, and measures the information received by the terminal.
  • the reference signal resources described above are used to obtain the beam metric parameters of each beam pair.
  • the terminal inputs the beam metric parameters corresponding to the agreed transmit beam and the first receive beam set into the neural network to obtain the beam prediction information corresponding to the beam pair composed of the agreed transmit beam and the preset receive beam set, and based on the The beam prediction information determines preferred beam pairs.
  • the terminal feeds back the beam metric parameters corresponding to the agreed transmit beam and the first receive beam set, the base station receives the agreed transmit beam and the beam metric parameters corresponding to the first receive beam set, and the base station uses the agreed transmit beam and the beam metric parameter corresponding to the first receive beam set.
  • Beam metric parameters corresponding to the transmit beam and the first receive beam set are input into the neural network to output beam prediction information corresponding to the agreed transmit beam and the preset receive beam set, and the preferred beam pair is determined based on the beam prediction information.
  • the beam prediction information here may be beam metric parameters, a general introduction to beam correspondence, or a preferred beam index.
  • the beam pair corresponding to the beam metric parameter with the largest beam metric parameter is regarded as the preferred beam pair, wherein the transmitting beam corresponding to the preferred beam pair is the target transmitting beam, and the receiving beam corresponding to the preferred beam pair is the target receiving beam. beam.
  • the transmitting end may use a target transmitting beam to transmit data or signals, and the receiving end may use a target receiving beam to receive data or signals.
  • the second receiving beam set is empty, and at this time, the preset receiving beam set is equal to the first receiving beam set.
  • the agreed transmission beam includes one of the following: the transmission beam with the lowest transmission beam index; the transmission beam with the highest transmission beam index; and the transmission beam with the largest corresponding beam metric parameter in the preset transmission beam set.
  • the agreed transmission beam may be pre-configured by the network side (eg, base station).
  • the agreed transmitting beam may be the transmitting beam with the lowest agreed beam index; in one example, the agreed transmitting beam may be the transmitting beam with the highest agreed beam index; in one example, the agreed transmitting beam may be It is the beam with the largest beam metric parameter among all transmitting beams.
  • the agreed transmission beam is a beam corresponding to an omnidirectional antenna or an omnidirectional antenna.
  • the agreed transmission beam is a default beam or a beam determined by the base station itself or the terminal confirms and instructs the base station according to channel conditions.
  • the preset receive beam set includes a first receive beam set and a second receive beam set, and the beam corresponding to the preset receive beam set is determined based on the beam metric parameters corresponding to the first receive beam set and the agreed transmit beam. Metric parameters.
  • the beam metric parameters corresponding to the first receive beam set are used to predict/determine the beam metric parameters corresponding to the union of the first receive beam set and the second receive beam set (ie, the preset receive beam set).
  • the transmitting end may use an agreed transmitting beam to transmit the reference signal
  • the receiving end may use a receiving beam in the first receiving beam set to receive the reference signal corresponding to the agreed transmitting beam, and send the reference signal corresponding to the first receiving beam set.
  • the beam metric parameters are used as the input of the neural network, and the beam metric parameters corresponding to the preset receiving beam set are output through the neural network.
  • the terminal feeds back the beam metric parameters corresponding to the first receiving beam set
  • the base station receives the beam metric parameters corresponding to the first receiving beam set and uses it as an input to the neural network, and outputs the preset reception through the neural network Beam metric parameters corresponding to the beam set.
  • the preferred beam pair is determined according to the beam metric parameters corresponding to the preset receiving beam set
  • the transmitting beam corresponding to the preferred beam pair is determined as the target transmitting beam
  • the receiving beam corresponding to the preferred beam pair is determined as target receiving beam.
  • the determination method of the preset receiving beam set includes but is not limited to: artificial intelligence method and interpolation method.
  • determining the target receive beam corresponding to the target transmit beam includes: determining the target receive beam based on a preset receive beam set and beam metric parameters corresponding to at least two agreed transmit beams.
  • the target receiving beam corresponding to the target transmitting beam may be determined through multiple measurements. Fix a transmit beam in advance, and determine a preferred receive beam based on the beam metric parameters corresponding to the preset receive beam set and the agreed transmit beam; then, fix a transmit beam, and determine a preferred receive beam based on the beam metric parameters corresponding to the preset receive beam set and the agreed transmit beam.
  • the agreed transmit beam determines a preferred receive beam; then, a final target receive beam is selected from the two preferred receive beams. You can also use the above method to determine three or more preferred receiving beams, and then determine a final target receiving beam from the three or more preferred receiving beams, so that the receiving end can transmit data or signals.
  • the communication method applied to the sending end also includes:
  • the corresponding relationship between transmitting beams and receiving beams refers to the binding relationship or mapping relationship between each transmitting beam and receiving beam.
  • a corresponding relationship can be established between each transmit beam and a receive beam, and then the beam index corresponding to the receive beam is found according to the beam index and binding relationship of the target transmit beam, and the receive beam corresponding to the beam index is used as target receiving beam.
  • the corresponding relationship between the transmitting beam and the receiving beam is determined based on at least one of historical data, a full-beam scanning method, and an artificial intelligence method.
  • historical data refers to the beam Scan historical data.
  • the historical data can be scanned data for one beam or multiple beams.
  • the full-beam scanning method refers to a method of scanning all beams. For example, the beam metric parameters of different transmitting beams and different receiving beams are obtained according to the all-beam scanning method, and the beam pair with the largest beam metric parameter is selected.
  • the artificial intelligence method refers to inputting the beam metric parameters corresponding to some beams into the neural network to predict/determine the corresponding Beam metric parameters, and determine the corresponding relationship between the transmitting beam and the receiving beam based on the beam metric parameters.
  • the beam pair with the largest metric parameter among the beam metric parameters is used as the preferred beam pair, and the preferred beam pair is the corresponding relationship between the transmitting beam and the receiving beam.
  • the correspondence between each transmit beam and the receive beam may be determined based on historical data, a full-beam scanning method, or an artificial intelligence method.
  • the corresponding relationship between the transmit beam and the receive beam includes one of the following: one transmit beam corresponds to one receive beam; one transmit beam is bound to one receive beam; one transmit beam is associated with one receive beam; one transmit beam Paired with a receive beam.
  • the correspondence between the transmitting beam and the receiving beam may be a one-to-one correspondence, a one-to-one binding relationship, a one-to-one association relationship, or a one-to-one pairing relationship.
  • the communication method applied to the sending end also includes:
  • the optimal beam pair is determined based on the artificial intelligence prediction method; the target receiving beam corresponding to the target transmitting beam is determined based on the optimal beam pair.
  • the optimal beam pair refers to the combination of the optimal transmit beam and the optimal receive beam.
  • the network side or the terminal side can obtain the optimal beam pair through AI, and then determine the optimal receiving beam based on the predicted optimal transmit beam (i.e., the target transmit beam) and the optimal beam pair, that is, determine the target reception Beam; then the transmitting end uses the optimal transmitting beam to send data or signals, and the receiving end uses the optimal receiving beam to receive data or signals.
  • determining the target receiving beam corresponding to the target transmitting beam includes:
  • the retriggering method is used to determine the target receiving beam corresponding to the target transmitting beam.
  • the retriggering method refers to the retriggering method.
  • the transmitting end can obtain the optimal transmitting beam based on the AI prediction method, and use the optimal transmitting beam to transmit reference signal resources to the receiving end, and trigger the receiving end to rescan the receiving beam, so that the receiving end determines the optimal receiving beam. And save the optimal receiving beam locally for transmitting data/signals.
  • the optimal receiving beam is the target receiving beam.
  • the communication method applied to the sending end also includes:
  • At least two preferred beam pairs or preferred transmitting beams are determined according to the artificial intelligence prediction method, at least two preferred beam pairs or preferred transmitting beams are measured to obtain the optimal Receive beam; use the optimal receive beam as the target receive beam corresponding to the target transmit beam.
  • at least two preferred beam pairs can be predicted through artificial intelligence, and then the optimal transmit beam among all preferred beam pairs is used to send the reference signal resources to the receiving end, so that the receiving end is in the process of receiving the reference signal resources. Scan the receiving beam to obtain the optimal receiving beam, and use the optimal receiving beam as the target receiving beam.
  • At least two preferred transmission beams can be directly predicted through artificial intelligence, and then each preferred transmission beam is used to send reference signal resources to the receiving end, so that the receiving end scans and receives the reference signal resources during the process.
  • Beam obtain the optimal receiving beam, and use the optimal receiving beam as the target receiving beam.
  • the beam metric parameters corresponding to all beam pairs can be sorted, and the beam pairs corresponding to the K beam metric parameters with the largest beam metric parameters can be used as the preferred beam pairs; alternatively, the beam metric parameters corresponding to all transmission beams can be sorted Sorting is performed, and the transmission beams corresponding to the K beam metric parameters with the largest beam metric parameters are used as the preferred transmission beams.
  • K is a positive integer.
  • the target receiving beam is determined according to the beam metric parameter array corresponding to the beam metric parameter, wherein the beam metric parameter includes at least one of the following: the agreed receive beam and the beam metric parameter corresponding to the first transmit beam set; 1. Beam metric parameters corresponding to the set of receive beams and the agreed transmit beam, beam metric parameters corresponding to the agreed receive beam and the preset transmit beam set; beam metric parameters corresponding to the preset receive beam set and the agreed transmit beam, Section 1 A beam metric parameter corresponding to a receive beam set and a first transmit beam set, a beam metric parameter corresponding to a first receive beam set and a preset transmit beam set, a beam corresponding to a preset receive beam set and the first transmit beam set Metric parameters.
  • the agreed receive beam is used as the target receive beam, and the target transmission is determined based on the beam metric parameters corresponding to the preset transmit beam set.
  • Beam scheme in one embodiment, when the beam metric parameters corresponding to the receive beam are input into the neural network, the agreed transmit beam is used as the target transmit beam, and the beam metric corresponding to the preset receive beam set is used The parameters and agreed transmit beam determine the target receive beam scheme.
  • the array corresponding to the beam metric parameters may be an array consisting of the beam metric parameters of the transmitting beam and the receiving beam.
  • the array corresponding to the beam metric parameters is composed of the beam metric parameters corresponding to each of the n1 transmit beams and each of the n2 receive beams. Two-dimensional array.
  • FIG. 2 is a flow chart of another communication method provided by an embodiment of the present application. This embodiment can be performed by the receiving end.
  • the receiving end may be a terminal.
  • the communication method in this embodiment includes: S210-S220.
  • the target reception beam can be obtained by receiving a target reception beam indication instruction sent by the transmitting end; in one embodiment, the second communication node obtains the target reception beam according to the measured beam metric parameters.
  • the beam metric parameters include at least one of the following: beam metric parameters corresponding to the agreed receive beam and the first transmit beam set; beam metric parameters corresponding to the first receive beam set and the agreed transmit beam, agreed The beam metric parameters corresponding to the receive beam set and the preset transmit beam set; the beam metric parameters corresponding to the preset receive beam set and the agreed transmit beam set, the beam metric parameters corresponding to the first receive beam set and the first transmit beam set, Beam metric parameters corresponding to the first receive beam set and the preset transmit beam set, and beam metric parameters corresponding to the preset receive beam set and the first transmit beam set.
  • the target receiving beam corresponds to the target transmitting beam
  • the target transmitting beam is a transmitting beam determined according to a preset transmitting beam set, wherein the preset transmitting beam set includes a first transmitting beam set and a second transmitting beam set.
  • the receiving end after the receiving end receives the target receiving beam indicated by the transmitting end, the receiving end transmits information through the target receiving beam.
  • the target reception beam is obtained according to receiving target reception beam indication signaling transmitted by the upper layer and/or the physical layer.
  • the target receiving beam corresponding to the target transmitting beam is indicated through high-layer signaling and/or physical layer signaling.
  • the target transmit beam is a transmit beam determined based on a preset transmit beam set, including: determining the target transmit beam based on an agreed receive beam and a beam metric parameter corresponding to the preset transmit beam set.
  • the beam metric parameters corresponding to the preset transmit beam set are determined based on the agreed receive beam and the beam metric parameters corresponding to the first transmit beam set.
  • the method of determining the target receiving beam corresponding to the target transmitting beam includes: determining the agreed receiving beam as the target receiving beam, and the agreed receiving beam includes one of the following: the receiving beam with the lowest receiving beam index; The receive beam with the highest beam index; in the preset receive beam set The corresponding receive beam with the largest beam metric parameter.
  • the method of determining the target receive beam corresponding to the target transmit beam includes: determining the target receive beam based on a preset receive beam set and the agreed beam metric parameters corresponding to the transmit beam.
  • the agreed transmission beam includes one of the following: the transmission beam with the lowest transmission beam index; the transmission beam with the highest transmission beam index; the transmission beam with the largest corresponding beam metric parameter in the preset transmission beam set.
  • the preset receive beam set includes a first receive beam set and a second receive beam set, and the beam corresponding to the preset receive beam set is determined based on the beam metric parameters corresponding to the first receive beam set and the agreed transmit beam. Metric parameters.
  • the method of determining the target receive beam corresponding to the target transmit beam includes: determining the target receive beam based on a preset receive beam set and beam metric parameters corresponding to at least two agreed transmit beams.
  • the communication method applied to the receiving end also includes:
  • the corresponding relationship between the transmitting beam and the receiving beam is determined based on at least one of historical data, a full-beam scanning method, and an artificial intelligence method.
  • the corresponding relationship between the transmit beam and the receive beam includes one of the following: one transmit beam corresponds to one receive beam; one transmit beam is bound to one receive beam; one transmit beam is associated with one receive beam; one transmit beam Paired with a receive beam.
  • the communication method applied to the receiving end also includes:
  • the optimal beam pair is determined based on the artificial intelligence prediction method; the target receiving beam corresponding to the target transmitting beam is determined based on the optimal beam pair.
  • the method of determining the target receiving beam corresponding to the target transmitting beam includes: using a retriggering method to determine the target receiving beam corresponding to the target transmitting beam.
  • the communication method applied to the receiving end also includes:
  • the target receiving beam is determined based on a beam metric parameter array corresponding to the beam metric parameter, wherein the beam metric parameter includes at least one of the following: an agreed receive beam and a beam metric parameter corresponding to the first transmit beam set ; Beam metric parameters corresponding to the first receive beam set and the agreed transmit beam, beam metric parameters corresponding to the agreed receive beam and the preset transmit beam set; Beam metric parameters corresponding to the preset receive beam set and the agreed transmit beam set, beam metric parameters corresponding to the first receive beam set and the first transmit beam set, beams corresponding to the first receive beam set and the preset transmit beam set Metric parameters are preset beam metric parameters corresponding to the receiving beam set and the first transmitting beam set.
  • each parameter in the communication method applied to the receiving end please refer to the description of the corresponding parameters in the embodiment of the communication method applied to the sending end, and will not be described again here.
  • an agreed receiving beam can be selected, and the repetition parameter is turned off.
  • the transmitter uses N1 transmitting beams to send reference signal resources, and uses the agreed receiving beam to receive reference signals corresponding to the N1 transmitting beams. resources, and obtain the beam metric parameters corresponding to N1 transmitting beams as the input of the neural network, and output the beam metric parameters corresponding to N transmitting beams.
  • the receiving terminal feeds back the beam metric parameters corresponding to the N1 transmit beams
  • the base station receives the beam metric parameters corresponding to the N1 transmit beams and uses them as the input of the neural network to output the beam metrics corresponding to the N transmit beams. parameter.
  • the transmitting end transmits data or signals according to at least one beam among the K preferred beams (ie, the target transmit beam), and the receiving end uses the agreed receive beam (ie, the target receive beam) to receive the transmitted data or signals.
  • the agreed receiving beam is configured by the base station.
  • the agreed receiving beam is the beam with the lowest agreed index.
  • the agreed receiving beam is the beam with the highest agreed index.
  • the agreed beam is the receiving beam with the largest beam metric parameter.
  • an agreed transmit beam is selected and the repetition parameter is turned on.
  • the transmitter uses the selected agreed transmit beam to send reference signal resources
  • the receiver uses M1 receive beams to receive the reference signal corresponding to the transmit beam. signal resources, and obtain the beam metric parameters corresponding to M1 receiving beams as the input of the neural network, output the beam metric parameters corresponding to M receiving beams, and select the K optimal beams corresponding to the M receiving beams as the preferred beams.
  • the receiving end feeds back the beam metric parameters corresponding to the M1 receiving beams
  • the base station receives the beam metric parameters corresponding to the M1 receiving beams as input to the neural network, and outputs the beam metric parameters corresponding to the M receiving beams. .
  • the receiving end receives data or signals according to at least one beam (ie, the target receiving beam) among the K preferred beams.
  • the agreed transmit beam i.e., the target transmit beam
  • the agreed transmit beam is configured by the base station.
  • the agreed transmit beam is the beam with the lowest agreed index.
  • the agreed transmit beam is the agreed index. The highest beam.
  • the agreed transmit beam is the transmit beam with the largest all beam metric parameters.
  • an agreed transmit beam i is selected, and the repetition parameter is turned on.
  • the transmitter uses the selected agreed transmit beam to send reference signal resources
  • the receiver uses M1 receive beams to receive the reference signal resources corresponding to the transmit beam.
  • Reference signal resources obtain beam metric parameters corresponding to M1 receiving beams as input to the neural network, and output beam metric values corresponding to M receiving beams.
  • select one optimal beam pair corresponding to the M receiving beams and the i-th transmitting beam as the optimal beam pair, denoted as Oi, where M1 and M are positive integers, and M1 ⁇ M, repeat the execution K times, i 1,...,K.
  • the transmitting end uses the optimal beam pair to send data or signals corresponding to the corresponding transmitting beam
  • the receiving end uses the optimal beam pair to send data or signals corresponding to the receiving beam.
  • the terminal needs to feed back the beam metric parameters corresponding to the i-th transmit beam and the M1 receive beams to the base station.
  • the base station receives the beam metric parameters and inputs them into the neural network to obtain the i-th transmit beam.
  • Beam and the optimal beam pair of the M1 receiving beams are repeatedly executed K times to obtain the globally optimal beam pair, and the receiving beam corresponding to the optimal beam pair is indicated to the user through high-level signaling or physical layer signaling.
  • Receiving end The base station uses the optimal beam to transmit data or signals to the corresponding transmit beam, and the receiving end uses the optimal beam to transmit data or signals to the corresponding receive beam.
  • each transmit beam and receive beam have a corresponding binding relationship.
  • This binding relationship can be obtained based on historical data, or scanning beams in the entire space, or learned through artificial intelligence prediction.
  • the target receiving beam used by the receiving end is determined based on the beam index and binding relationship of the target transmitting beam.
  • the transmitting end uses the transmitting beam corresponding to the optimal transmitting beam (i.e., the target transmitting beam) to send data or signals, and the receiving end uses the optimal receiving beam (i.e., the target receiving beam) to correspond to the receiving beam to transmit data or signals.
  • the optimal beam is obtained based on prediction.
  • the base station or terminal simultaneously obtains the optimal transmit beam and receive beam pair through AI prediction, and determines the optimal beam based on the predicted transmit beam and receive beam pair.
  • the sending end uses the sending beam corresponding to the optimal beam to send data or signals
  • the receiving end uses the receiving beam corresponding to the optimal beam to send data or signals.
  • the transmitting end obtains the optimal transmitting beam T0 (ie, the target transmitting beam) based on the prediction method.
  • the receiving end receives the reference signal and performs Beam scanning selects the beam with the largest beam metric parameter as the optimal receiving beam (i.e., the target receiving beam).
  • the transmitting end uses the transmitting beam corresponding to the optimal transmitting beam to send data or signals, and the receiving end uses the receiving beam corresponding to the optimal receiving beam to transmit data or signals.
  • the transmitting end or the receiving end predicts K preferred beams.
  • the base station predicts K preferred beam pairs/or preferred transmit beams based on the reported beam metric parameters, and performs measurements based on these K beam transmit reference signal resources to obtain K*M beam metric parameters.
  • the terminal selects Select the beam with the largest beam metric parameter among K*M as the preferred beam pair.
  • the transmitting end uses the optimal beam to send data or signals to the corresponding transmitting beam
  • the receiving end uses the optimal beam to transmit data or signals to the corresponding receiving beam.
  • K is an integer
  • M is the number of receiving beams.
  • FIG. 3 is a structural block diagram of a communication device provided by an embodiment of the present application. This embodiment applies to the sending end. As shown in Figure 3, the communication device in this embodiment includes: a first determining module 310 and a sending module 320.
  • the first determining module 310 is configured to determine the target receiving beam corresponding to the target transmitting beam; wherein the target transmitting beam is a transmitting beam determined according to a preset transmitting beam set, wherein the preset transmitting beam set includes a first transmitting beam set and a third transmitting beam set. Two transmit beam sets.
  • the sending module 320 is configured to send target reception beam indication signaling.
  • the target reception beam indication signaling is used to instruct the receiving end to determine the target reception beam and use the determined target reception beam for information transmission.
  • the target reception beam indication signaling is sent through high-layer signaling and/or physical layer signaling.
  • the target transmit beam is a transmit beam determined based on a preset transmit beam set, including: determining the target transmit beam based on an agreed receive beam and a beam metric parameter corresponding to the preset transmit beam set.
  • the beam metric parameters corresponding to the preset transmit beam set are determined based on the agreed receive beam and the beam metric parameters corresponding to the first transmit beam set.
  • the first determination module 310 includes: determining the agreed receiving beam as the target receiving beam, and the agreed receiving beam includes one of the following: the receiving beam with the lowest receiving beam index; the receiving beam with the highest receiving beam index. ;The receiving beam with the largest corresponding beam metric parameter in the preset receiving beam set.
  • the first determination module 310 includes: determining the target receiving beam according to the preset receiving beam set and the beam metric parameters corresponding to the agreed transmit beams.
  • the agreed transmission beam includes one of the following: the transmission beam with the lowest transmission beam index; the transmission beam with the highest transmission beam index; the transmission beam with the largest corresponding beam metric parameter in the preset transmission beam set.
  • the preset receive beam set includes a first receive beam set and a second receive beam set, and the beam corresponding to the preset receive beam set is determined based on the beam metric parameters corresponding to the first receive beam set and the agreed transmit beam. Metric parameters.
  • the first determination module 310 includes: determining a target receiving beam based on a preset receiving beam set and beam metric parameters corresponding to at least two agreed transmit beams.
  • the communication device applied to the sending end also includes:
  • the second determination module is configured to determine the corresponding relationship between the transmitting beam and the receiving beam; the third determining module is configured to determine the target receiving beam according to the corresponding relationship and the beam index of the target transmitting beam.
  • the corresponding relationship between the transmitting beam and the receiving beam is determined based on at least one of historical data, a full-beam scanning method, and an artificial intelligence method.
  • the corresponding relationship between the transmit beam and the receive beam includes one of the following: one transmit beam corresponds to one receive beam; one transmit beam is bound to one receive beam; one transmit beam is associated with one receive beam; one transmit beam Paired with a receive beam.
  • the communication device applied to the sending end also includes:
  • the fourth determination module is configured to determine the optimal beam pair based on the artificial intelligence prediction method; the fifth determination module is configured to determine the target receiving beam corresponding to the target transmitting beam based on the optimal beam pair.
  • the first determining module 310 includes:
  • the retriggering method is used to determine the target receiving beam corresponding to the target transmitting beam.
  • the communication device applied to the sending end also includes:
  • the sixth determination module is configured to determine at least two preferred beam pairs or preferred transmitting beams based on the artificial intelligence prediction method; and determine the target receiving beam based on the at least two preferred beam pairs or preferred transmitting beams.
  • the target receiving beam is determined according to the beam metric parameter array corresponding to the beam metric parameter, wherein the beam metric parameter includes at least one of the following: the agreed receive beam and the beam metric parameter corresponding to the first transmit beam set; 1. Beam metric parameters corresponding to the set of receive beams and the agreed transmit beam, beam metric parameters corresponding to the agreed receive beam and the preset transmit beam set; beam metric parameters corresponding to the preset receive beam set and the agreed transmit beam, Section 1 A beam metric parameter corresponding to a receive beam set and a first transmit beam set, a beam metric parameter corresponding to a first receive beam set and a preset transmit beam set, a beam corresponding to a preset receive beam set and the first transmit beam set Metric parameters.
  • the communication device provided by this embodiment is configured to implement the communication method applied to the sending end in the embodiment shown in Figure 1.
  • the implementation principles and technical effects of the communication device provided by this embodiment are similar and will not be described again here.
  • FIG. 4 is a structural block diagram of another communication device provided by an embodiment of the present application. This embodiment is applied to the receiving end. As shown in Figure 4, the communication device in this embodiment includes: an acquisition module 410 and transmission module 420.
  • the acquisition module 410 is configured to acquire the target reception beam; the transmission module 420 is configured to transmit information according to the acquired target reception beam; the target reception beam corresponds to the target transmission beam, and the target transmission beam is the transmission determined according to the preset transmission beam set beam, wherein the preset transmission beam set includes a first transmission beam set and a second transmission beam set.
  • the target reception beam is obtained according to receiving target reception beam indication signaling transmitted by the upper layer and/or the physical layer.
  • the target transmit beam is a transmit beam determined based on a preset transmit beam set, including: determining the target transmit beam based on an agreed receive beam and a beam metric parameter corresponding to the preset transmit beam set.
  • the beam metric parameters corresponding to the preset transmit beam set are determined based on the agreed receive beam and the beam metric parameters corresponding to the first transmit beam set.
  • the method of determining the target receiving beam corresponding to the target transmitting beam includes: determining the agreed receiving beam as the target receiving beam, and the agreed receiving beam includes one of the following: the receiving beam with the lowest receiving beam index; The receiving beam with the highest beam index; the receiving beam with the largest corresponding beam metric parameter in the preset receiving beam set.
  • the method of determining the target receive beam corresponding to the target transmit beam includes: determining the target receive beam based on a preset receive beam set and the agreed beam metric parameters corresponding to the transmit beam.
  • the agreed transmission beam includes one of the following: the transmission beam with the lowest transmission beam index; the transmission beam with the highest transmission beam index; the transmission beam with the largest corresponding beam metric parameter in the preset transmission beam set.
  • the preset receive beam set includes a first receive beam set and a second receive beam set, and the beam corresponding to the preset receive beam set is determined based on the beam metric parameters corresponding to the first receive beam set and the agreed transmit beam. Metric parameters.
  • the method of determining the target receive beam corresponding to the target transmit beam includes: determining the target receive beam based on a preset receive beam set and beam metric parameters corresponding to at least two agreed transmit beams.
  • the communication device used at the receiving end also includes:
  • the first determination module is configured to determine the corresponding relationship between the transmitting beam and the receiving beam; the second determining module is configured to determine the target receiving beam according to the corresponding relationship and the beam index of the target transmitting beam.
  • the corresponding relationship between the transmitting beam and the receiving beam is determined based on at least one of historical data, a full-beam scanning method, and an artificial intelligence method.
  • the corresponding relationship between the transmit beam and the receive beam includes one of the following: one transmit beam corresponds to one receive beam; one transmit beam is bound to one receive beam; one transmit beam is associated with one receive beam; one transmit beam Paired with a receive beam.
  • the communication device used at the receiving end further includes:
  • the third determination module is configured to determine the optimal beam pair based on the artificial intelligence prediction method; the fourth determination module is configured to determine the target receiving beam corresponding to the target transmitting beam based on the optimal beam pair.
  • the method of determining the target receiving beam corresponding to the target transmitting beam includes: using a retriggering method to determine the target receiving beam corresponding to the target transmitting beam.
  • the communication device used at the receiving end further includes:
  • the fifth determination module is configured to determine at least two preferred beam pairs or preferred transmitting beams based on the artificial intelligence prediction method; the sixth determination module is configured to determine the target receiving beam based on the at least two preferred beam pairs or preferred transmitting beams.
  • the target receiving beam is determined based on a beam metric parameter array corresponding to the beam metric parameter, wherein the beam metric parameter includes at least one of the following: an agreed receive beam and a beam metric parameter corresponding to the first transmit beam set ; Beam metric parameters corresponding to the first receive beam set and the agreed transmit beam, beam metric parameters corresponding to the agreed receive beam and the preset transmit beam set; beam metric parameters corresponding to the preset receive beam set and the agreed transmit beam , the beam metric parameters corresponding to the first receive beam set and the first transmit beam set, the beam metric parameters corresponding to the first receive beam set and the preset transmit beam set, the preset receive beam set and the first transmit beam set corresponding beam metric parameters.
  • the communication device provided by this embodiment is configured to implement the communication method applied to the receiving end of the embodiment shown in Figure 2.
  • the implementation principles and technical effects of the communication device provided by this embodiment are similar and will not be described again here.
  • FIG. 5 is a schematic structural diagram of a communication device provided by an embodiment of the present application.
  • the device provided by this application includes: a processor 510 and a memory 520.
  • the number of processors 510 in the device may be one or more.
  • one processor 510 is taken as an example.
  • the number of memories 520 in the device may be one or more.
  • one memory 520 is taken as an example.
  • the processor 510 and the memory 520 of the device can be connected through a bus or other means. In Figure 5, the connection through the bus is taken as an example.
  • the communication device may serve as the sending end.
  • the memory 520 can be configured to store software programs, computer-executable programs and modules, such as program instructions/modules corresponding to the equipment of any embodiment of the present application (for example, applied in the communication device of the sending end). first determining module 310 and sending module 320).
  • the memory 520 may include a program storage area and a data storage area, where the program storage area may store an operating system, at least An application required to function; the storage data area can store data created based on the use of the device, etc.
  • memory 520 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device.
  • memory 520 may further include memory located remotely relative to processor 510, and these remote memories may be connected to the device through a network.
  • Examples of the above-mentioned networks include but are not limited to the Internet, intranets, local area networks, mobile communication networks and combinations thereof.
  • the device provided above can be configured to execute the communication method provided by any of the above embodiments and applied to the sending end, and has corresponding functions and effects.
  • the device provided above can be configured to execute the communication method provided by any of the above embodiments and applied to the receiving end, and has corresponding functions and effects.
  • Embodiments of the present application also provide a storage medium containing computer-executable instructions.
  • the computer-executable instructions When executed by a computer processor, the computer-executable instructions are used to perform a communication method applied to the transmitting end.
  • the method includes: determining the target transmission beam corresponding to Target receive beam; wherein, the target transmit beam is a transmit beam determined according to a preset transmit beam set, wherein the preset transmit beam set includes a first transmit beam set and a second transmit beam set; and the target receive beam indication signaling is sent, so
  • the target receiving beam indication signaling is used to instruct the receiving end to determine the target receiving beam, and use the determined target receiving beam to transmit information.
  • Embodiments of the present application also provide a storage medium containing computer-executable instructions.
  • the computer-executable instructions When executed by a computer processor, the computer-executable instructions are used to perform a communication method applied to a receiving end.
  • the method includes: acquiring a target receiving beam; according to The acquired target receiving beam performs information transmission; the target receiving beam corresponds to the target transmitting beam, and the target transmitting beam is a transmitting beam determined according to a preset transmitting beam set, wherein the preset transmitting beam set includes a first transmitting beam set and a second Transmit beam set.
  • user equipment encompasses any suitable type of wireless user equipment, such as a mobile phone, a portable data processing device, a portable web browser or a vehicle-mounted mobile station.
  • the various embodiments of the present application may be implemented in hardware or special purpose circuitry, software, logic, or any combination thereof.
  • some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software that may be executed by a controller, microprocessor, or other computing device, although the application is not limited thereto.
  • Embodiments of the present application may be implemented by a data processor of the mobile device executing computer program instructions, for example in a processor entity, or by hardware, or by a combination of software and hardware.
  • Computer program instructions may be assembly instructions, Instruction Set Architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or in the form of a Source or object code written in any combination of one or more programming languages.
  • ISA Instruction Set Architecture
  • Any block diagram of a logic flow in the figures of this application may represent program steps, or may represent interconnected logic circuits, modules, and functions, or may represent a combination of program steps and logic circuits, modules, and functions.
  • Computer programs can be stored on memory.
  • the memory may be of any type suitable for the local technical environment and may be implemented using any suitable data storage technology, such as but not limited to Read-Only Memory (ROM), Random Access Memory (RAM), optical Storage devices and systems (Digital Video Disc (DVD) or Compact Disk (CD)), etc.
  • Computer-readable media may include non-transitory storage media.
  • the data processor can be any type suitable for the local technical environment, such as but not limited to general-purpose computers, special-purpose computers, microprocessors, digital signal processors (Digital Signal Processing, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC ), programmable logic devices (Field-Programmable Gate Array, FPGA) and processors based on multi-core processor architecture.
  • DSP Digital Signal Processing
  • ASIC Application Specific Integrated Circuit
  • FPGA Field-Programmable Gate Array

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Abstract

本申请提出一种通信方法、设备和存储介质。应用于发送端的通信方法，包括：确定目标发送波束对应的目标接收波束；其中，所述目标发送波束为根据预设发送波束集合确定的发送波束，其中，所述预设发送波束集合包括第一发送波束集合和第二发送波束集合；发送目标接收波束指示信令，所述目标接收波束指示信令用于指示接收端确定目标接收波束，并用所述确定的目标接收波束进行信息传输。

Description

通信方法、设备和存储介质 技术领域
本申请涉及通信领域，例如涉及一种通信方法、设备和存储介质。
背景技术
将人工智能(Artificial Intelligence，AI)/机器学习(Machine learning，ML)引入无线通信系统已经得到广泛的共识。比如研究的内容包括但不限于信道状态信息反馈，波束管理，信道估计，定位，干扰管理，用户调度等，对于波束管理，包括但不限于波束训练、波束跟踪、波束恢复几个方面，需要解决的核心问题是如何通过尽可能低的控制开销获取准确的波束对。
相关技术中，解决高频的覆盖问题，主要用到了波束赋形，将发送能量集中于用户方向以获得增益。一般来说，包括发送端的N个发送波束，接收端的M个接收波束，最多需要进行波束扫描N*M次就能选出最优的波束，如果分阶段进行扫描，比如固定一个接收波束扫描不同的N个发送波束，从而选出最优的发送波束，然后基于最优的发送波束，固定发送波束，扫描M次不同的接收波束，从而选择出最优的接收波束。从而可以通过至少N+M次波束扫描得到一个较优的收发波束对。但随着载频的进一步提高，波束的个数会进一步增加，即M和N至少有一个非常大时，需要的参考信号资源开销也是非常大的。且这种分阶段扫描的方式并不能找到全局的最优的波束对。一种方法是通过人工智能的方式，通过N1个发送波束来预测N个发送波束中的最优值和/或用M1个接收波束来预测M个接收波束中的最优值。其中N1<N，M1<M。但预测的发送波束由于实际中并没有传输，所以，接收端无法确定对应的优选的接收波束是什么。
发明内容
本申请实施例提供一种通信方法，应用于发送端，包括：
确定目标发送波束对应的目标接收波束；其中，所述目标发送波束为根据预设发送波束集合确定的发送波束，其中，所述预设发送波束集合包括第一发送波束集合和第二发送波束集合；发送目标接收波束指示信令，所述目标接收波束指示信令用于指示接收端确定目标接收波束，并用所述确定的目标接收波束进行信息传输。
本申请实施例提供一种通信方法，应用于接收端，包括：
获取目标接收波束；根据获取的目标接收波束进行信息传输；所述目标接收波束与目标发送波束相对应，所述目标发送波束为根据预设发送波束集合确定的发送波束，其中，所述预设发送波束集合包括第一发送波束集合和第二发送波束集合。
本申请实施例提供一种通信设备，包括：存储器，以及一个或多个处理器；
所述存储器，配置为存储一个或多个程序；当所述一个或多个程序被所述一个或多个处理器执行，使得所述一个或多个处理器实现上述任一实施例所述的方法。
本申请实施例提供一种存储介质，所述存储介质存储有计算机程序，所述计算机程序被处理器执行时实现上述任一实施例所述的通信方法。
附图说明
图1是本申请实施例提供的一种通信方法的流程图；
图2是本申请实施例提供的另一种通信方法的流程图；
图3是本申请实施例提供的一种通信装置的结构框图；
图4是本申请实施例提供的另一种通信装置的结构框图；
图5是本申请实施例提供的一种通信设备的结构示意图。
具体实施方式
下文中将结合附图对本申请的实施例进行说明。以下结合实施例附图对本申请进行描述，所举实例仅用于解释本申请。
在本申请实施例中，移动通信网络(包括但不限于第三代移动通信技术(the3rd Generation mobile communication technology，3G)，4G，5G以及未来移动通信网络)的网络架构可以包括网络侧设备(例如包括但不限于基站)和接收侧设备(例如包括但不限于终端)。在本示例中，在下行链路中第一通信节点(也可以称为第一通信节点设备)可以是基站侧设备，第二通信节点(也可以称为第二通信节点设备)可以是终端侧设备。在上行链路中第一通信节点可以是终端侧设备，第二通信节点可以是基站侧设备。在两个通信节点是设备到设备通信中，第一通信节点和第二通信节点都可以是基站或者终端。
在本申请实施例中，基站可以是长期演进(Long Term Evolution，LTE)，长期演进增强(Long Term Evolution Advanced，LTEA)中的演进型基站(Evolutional Node B，eNB或eNodeB)、5G网络中的基站设备、或者未来通 信系统中的基站等，基站可以包括各种宏基站、微基站、家庭基站、无线拉远、路由器、无线保真(Wireless Fidelity，WIFI)设备或者主小区(primary cell)和协作小区(secondary cell)等各种网络侧设备，定位管理功能(location management function，LMF)设备。
本申请实施例中，终端是一种具有无线收发功能的设备。可以部署在陆地上，包括室内或室外、手持、穿戴或车载；也可以部署在水面上(如轮船等)；还可以部署在空中(例如飞机、气球和卫星上等)。终端可以是手机(mobile phone)、平板电脑(Pad)、带无线收发功能的电脑、虚拟现实(Virtual Reality，VR)终端、增强现实(Augmented Reality，AR)终端、工业控制(Industrial Control)中的无线终端、无人驾驶(self driving)中的无线终端、远程医疗(remote medical)中的无线终端、智能电网(smart grid)中的无线终端、运输安全(transportation safety)中的无线终端、智慧城市(smart city)中的无线终端、智慧家庭(smart home)中的无线终端等等。本申请的实施例对应用场景不作限定。终端有时也可以称为用户、用户设备(User Equipment，UE)、接入终端、UE单元、UE站、移动站、移动台、远方站、远程终端、移动设备、UE终端、无线通信设备、UE代理或UE装置等。本申请实施例并不进行限定。
本申请实施例中，高层信令包括但不限于无线资源控制(Radio Resource Control，RRC)，媒体接入控制-控制单元(Media Access Control-Control Element，MAC CE)；基站和终端之间还可以传输物理层信令，比如，下行链路在物理下行控制信道(Physical Downlink Control CHannel，PDCCH)上传输物理层信令，上行链路在物理上行控制信道(Physical Uplink Control CHannel，PUCCH)传输物理层信令，物理随机接入信道(Physical random-access channel，PRACH)。
本申请实施例中，各种参数的指示(Indicator)，也可以称为索引(Index)，或者标识(Identifier，ID)，它们是完全等价的概念。比如无线系统的资源标识，这里无线系统资源包括但不限于以下之一：一个参考信号资源、参考信号资源组，参考信号资源配置、信道状态信息(Channel State Information，CSI)报告、CSI报告集合、终端、基站、面板、神经网络、子神经网络、神经网络层等对应的索引。基站可以通过各种高层信令或者物理层信令指示一个或一组资源的标识给终端。
本申请实施例中，波束集合也可以称为波束集。波束包括发送波束和/或接收波束。
在本申请的一些实施例中，波束扫描的一个过程可以是：至少一个发送波束发送对应的参考信号资源，至少一个接收波束接收所述至少一个发送波束发送的参考信号资源，并计算至少一个接收波束和至少一个发送波束对应的波束 度量参数，根据所述波束度量参数确定优选的K个波束对。一个波束对包括一个发送波束和一个接收波束，K为正整数。在一个示例中，这里的波束可以替换成天线或者端口。
本申请实施例中，传输包括发送或接收。比如发送或者接收数据，发送或者接收信号。
在一些实施例中，为了计算信道状态信息或者进行信道估计，移动性管理，定位等，需要基站或者用户发送参考信号(Reference Signal，RS)，参考信号包括但不限于定位参考信号(Positioning Reference Signal，PRS)，信道状态信息参考信号(Channel-State Information reference signal，CSI-RS)，同步信号块(Synchronization Signals Block，SSB)、物理广播信道(Physical Broadcast Channel，PBCH)、同步广播块/物理广播信道(SSB/PBCH)。其中，CSI-RS包括零功率的CSI-RS(Zero Power CSI-RS，ZP CSI-RS)和非零功率的CSI-RS(Non-Zero Power CSI-RS，NZP CSI-RS)，信道状态信息干扰测量信号(Channel-State Information-Interference Measurement，CSI-IM)，探测参考信号(Sounding Reference Signal，SRS)，NZP CSI-RS可以用来测量信道或者干扰，CSI-RS也可以用来做跟踪，叫做跟踪参考信号(CSI-RS for Tracking，TRS)，而CSI-IM一般用来测量干扰，SRS用来进行上行信道估计和CSI测量CSI-RS，SRS，CSI-IM等这些参考信号。SSB和/或PBCH可以统称为SSB。在传输时又包括时域特性，其中时域特性包括但不限于非周期(aperiodic)、周期(periodic)、半持续(semi-persistent)特性，分别表示传输的参考信号是非周期传输的，周期传输的，或者半持续传输的。其中，周期参考信号或者半持续的参考信号都会通过高层信令配置一个周期和/或时隙偏置(slot offset)信息，这两个参数可以是联合编码的(比如通过高层信令periodicity And Offset配置，通过获取这个参数，用户就可以知道周期或者半持续参考信号的传输周期，以及传输的时隙slot)。在通信系统中，传输参考信号的资源可以称为参考信号资源，为了节省信令开销等，可以把多个参考信号资源分成多个集合(比如CSI-RS resource set，CSI-IM resource set，SRS resource set)，参考信号资源集合包括至少一个参考信号资源，而多个参考信号资源集合可以都来自同一个参考信号资源设置(比如CSI-RS resource setting，SRS resource setting，CSI-IM resource setting，其中CSI-IM resource setting可能和CSI-IM resource setting合并，都称为CSI-RS resource setting)来配置参数信息。
在一些实施例中，基站配置测量资源信息，测量资源信息用于获取信道状态信息。其中，测量资源信息包括至少一个信道测量资源(Channel Measurement Resource，CMR)信息和至少一个干扰测量资源(Interference Measurement Resource，IMR)信息。基站在一个报告配置(report config)或报告设置(reporting  setting)中配置测量资源信息。其中CMR信息用于使终端对各波束的信道状态进行测量，IMR信息用于使终端对各波束所受到的干扰进行测量。
在一些实施例中，人工智能(Artificial Intelligence，AI)包括机器学习(Machine learning，ML)，深度学习，强化学习，迁移学习，深度强化学习，元学习等具有自我学习的设备、组件、软件、模块。在一些实施例中，人工智能通过人工智能网络(或称为神经网络)实现，神经网络包括多个层，每层包括至少一个节点。在一个示例中，神经网络包括输入层，输出层，至少一层隐藏层，其中每层神经网络包括但不限于使用了全连接层、稠密层、卷积层、转置卷积层、直连层、激活函数、归一化层、池化层等至少之一。在一些实施例中，神经网络的每一层可以包括一个子神经网络，比如残差块(Residual Network block，ResNet block)，稠密网络(DenseNet Block)，循环网络(Recurrent Neural Network，RNN)等。人工智能网络包括神经网络模型和/或神经网络模型对应的神经网络参数，其中，神经网络模型可以简称为网络模型，神经网络参数可以简称网络参数。一个网络模型定义了神经网络的层数，每层的大小，激活函数，连接情况，卷积核和大小卷积步长，卷积类型(比如1D卷积、2D卷积、3D卷积、空心卷积、转置卷积、可分卷积、分组卷积、扩展卷积等)等网络的架构，而网络参数是网络模型中每层网络的权值和/或偏置以及它们的取值。一个网络模型可以对应多套不同的神经网络参数取值以适应不同的场景。网络参数的取值可以通过线下训练和/或在线训练的方式获得。一个神经网络模型可以对应多个不同的神经网络参数取值。
在一些实施例中，特别是在高频传输时，由于载频比较高，路径损失大，需要用到波束赋形，将能量集中在朝终端的方向传播，从而需要用到波束管理。其中波束管理包括但不限于波束扫描、波束跟踪和波束恢复几个方面，需要解决的核心问题是如何通过尽可能低的控制开销获取准确的波束对。其中，波束扫描包括发送端波束扫描和/或接收端波束扫描。为了减小波束扫描的开销，可以通过两阶段的扫描。在一些实施例中，波束训练可以包括P1，P2，P3的训练。其中，在P1中同时扫描发送端的波束和接收端的波束；而在P2阶段的波束扫描中会固定一个接收波束，并扫描不同的发送波束；P3阶段为固定一个发送波束，扫描不同的接收波束。在一个示例中，比如通过发送N个波束，固定接收，重复参数repetition取值为off，然后测量其中的N个波束对应的RSRP，找到优选的L个进行上报。在一个示例中，通过发送1个波束，M个接收波束接收，重复参数取值为on，然后测量其中的M个波束对应的RSRP，找到最优的L个进行上报。在N和M都很大的时候，开销是非常大的，所以需要非常大的开销。在一些示例中，可以用到AI的波束预测功能，即只输入L0个波束对应的波束度量参数，但根据L0波束度量参数预测L1个波束对应的波束度量参数，这里， 所述的L1个波束可以包括所述的L0个波束，其中，L0<L1，且它们都是正整数，波束可以为发送波束、接收波束或发送接收波束对。每个波束可以对应一个波束数方向，这里，N，M，L，L1，L0均为正整数。
在一些实施例中，波束包括发送波束、接收波束、预编码、预编码矩阵、预编码矩阵索引，接收波束和发送波束对，发送波束和接收波束对。所述波束可以为一种资源(例如发端预编码，收端预编码、天线端口，天线权重矢量，天线权重矩阵等)，波束索引可以被替换为资源索引，因为波束可以与一些时频资源进行传输上的绑定。波束也可以为一种传输(发送/接收)方式；所述的传输方式可以包括空分复用、频域/时域分集等。所述接收波束指示是指，发送端可以通过当前参考信号资源(或参考信号资源索引)和天线端口，与UE反馈报告的参考信号资源(或基准参考信号资源，参考信号资源索引)和天线端口的准共址(Quasi-Co-Location Indicator，QCL)假设来进行指示。波束对包括一个发送波束和一个接收波束的组合。
在一些实施例中，波束方向或者波束角度可以包括以下至少之一：到达角(Angle Of Arrival，AOA)、离开角(Angle Of Departure，AOD)、ZOD(Zenith angle Of Departure)、ZOA(Zenith angle Of Arrival)、由AOA，AOD，ZOD，ZOA至少一个角度构造的向量或者向量索引，离散傅里叶变化(Discrete Fourier Transformation，DFT)矢量、码本中的码字、发送波束索引、接收波束索引、发送波束组索引、接收波束组索引。在一些实施例中，波束指一个空域滤波器或者一个空间接收/发送参数，空域滤波可以是以下至少之一：DFT矢量，预编码矢量，DFT矩阵，预编码矩阵，或者多个DFT线性组合构成的矢量，多个预编码矢量线性组合构成的矢量。
在一些示例中，用于预测波束度量参数的AI设备通过一个神经网络实现。L0个波束对应的波束度量参数组合成一个波束度量参数数组(第一波束度量参数数组)输入神经网络，神经网络输出L1个波束对应的波束度量参数数组(第二波束度量参数数组)，并通过所述L1个波束对应的波束度量参数数组中的最大波束度量参数对应的索引确定最优的波束。其中，L1一般来说大于L0，且都为正整数。
在一些示例中，通过线上训练或者线下训练的方式获得神经网络的参数。比如通过输入至少一个样本和标签，训练所述的神经网络模型获得神经网络参数。在一些示例中，样本为一个终端测量得到的一个第一波束度量参数数组，标签为一个终端测量得到的一个第一波束度量参数数组对应的第二波束度量参数数组。在训练网络时，所述的第一波束度量参数数组和所述的第二波束度量参数数组具有对应关系，例如为一一对应关系。在进行神经网络部署或者测试 阶段，通过将第一波束度量参数数组输入神经网络以输出一个预测的第二波束度量参数数组，比较预测的第二波束度量参数数组和标签对应的第二波束度量参数数组，就可以知道网络的预测性能，以及根据两者的损失函数来训练神经网络。
在一些示例中，将发送波束和/或接收波束索引按约定的方式编号，形成波束索引。其中，一个波束索引包括以下之一：发送波束索引，接收波束索引，发送接收波束对索引。一个波束索引对应着一个波束方向，或者波束方向对应的矢量或矩阵。终端接收参考信号(比如CSI-RS，SSB等)并测量每个波束对应的波束度量参数，并按波束索引的大小进行排序，得到波束度量参数数组。一般来说，第一波束度量参数数组为第一波束集合对应的波束度量参数形成的波束度量参数数组，第二波束度量参数数组为第二波束集合对应的波束度量参数形成的波束度量参数数组。而第一波束集合为第二波束集合的一个子集合。
在一些示例中，需要对第一波束度量参数数组中的元素进归一化处理，以便于神经网络更快的收敛。所谓归一化是指将一个数组里的元素取值归一化到一个大于或等于a且小于或等于b的区间的一个值。在一个示例中，a＝-0.5，b＝0.5；在一个示例中，a＝0，b＝1；在一个示例中，将数组里的元素除以这个数组元素里的绝对值最大的数以实现归一化；在一个示例中，将数组里的元素除以这个数组元素里的方差以实现归一化；在一个示例中，将数组里的元素除以一个固定的值(比如所有样本里的所有元素的最大值)以实现归一化；在一个示例中，将数组里的元素除以一个统计的值(比如所有样本里的所有元素的统计的方差值)以实现归一化。对于索引值，比如波束索引，CRI，SSBRI等，可以通过独热编码(One-Hot Encoding)实现归一化。
在一些示例中，波束度量参数数组是2维的数组，比如是一个向量。在一些示例中，波束度量参数数组是二维的数组，比如是一个矩阵。在一些示例中，波束度量参数数组是大于二维的数组，比如是一个张量。其中，向量和矩阵也可以看成张量的一种特殊情况。
在一些实施例中，波束度量参数为至少一个波束对应的层1的参考信号接收功率(L1Reference Signal Received Power，L1-RSRP或RSRP)；在一些实施例中，波束度量参数为至少一个波束对应的层1的参考信号信干噪比(L1Signal-to-Interference Noise Ratio，L1-SINR或SINR)；在一些实施例中，波束度量参数为至少一个波束对应的参考信号接收质量(Reference Signal Received Quality，RSRQ)；在一些实施例中，波束度量参数为至少一个波束对应的波束角度(AOA，ZOA，AOD，ZOD等至少之一，有时也分别称为水平到达角度，垂直到达角，水平离开角，垂直离开角)；在一些实施例中，波束度量参数为 至少一个波束对应的发送波束索引；在一些实施例中，波束度量参数为至少一个波束对应的接收波束索引；在一些实施例中，波束度量参数为至少一个波束对应的发送波束和接收波束对索引(简称为波束对索引或波束对)；在一些实施例中，波束度量参数为至少一个波束对应的波束域接收功率映射(Beam Domain Receive Power Map，BDRPM)；在一些实施例中，波束度量参数为至少一个波束对应的信道状态信息参考信号资源指示(CSI-RS Resource Indicator，CRI)；在一些实施例中，波束度量参数为至少一个波束对应的同步信号块资源指示(Synchronization Signals Block Resource Indicator，SSBRI)。在一些实施例中，波束度量参数为至少一个波束对应的以下波束度量参数的至少两个的组合：RSRP、RSRQ、SINR、波束角度、发送波束索引，接收波束索引，波束对索引、CRI，SSBRI等。在一些实施例中，波束度量参数为RSRP、RSRQ、SINR之一的线性值。在一些实施例中，波束度量参数为RSRP、RSRQ、SINR之一的对数值或者叫分贝值(DB)。
在一些实施例中，波束度量参数基于CSI-RS测量得到的。在一些实施例中，波束度量参数基于SSB测量得到的，在一些实施例中波束度量参数基于SRS测量得到的。
在一个实施例中，网络侧设备包括至少1个发送面板(Panel)，每个面板有4行8列的阵子，对应Nt＝32个发送波束(Txbeam)，终端包括Npr＝2个接收面板，每个面板包括2行4列个阵子，对应Nr＝8个接收波束(Rxbeam)，总共对应N＝2(panel)*8(Rxbeam)*32(Txbeam)＝512个波束对。每个波束对可以通过接收该波束对应的参考信号获得对应的波束度量参数(比如RSRP)。但实际中，如果每次进行波束扫描都要发送512个波束对应的参考信号资源，那参考信号资源的开销太大了。有一个方法是用全部N＝512个波束的一个波束子集合，即M个波束来预测所有N波束上的波束度量参数(比如RSRP)。并根据波束度量参数(比如RSRP)来确定优选的接收波束和/或发送波束，比如选择波束度量参数数组中RSRP最大的K个RSRP对应的波束为优选波束。其中Nr、Nt、N、M可以为正整数。用K个优选的波束中的至少一个波束进行数据或者信号传输。这里波束包括发送波束和/或接收波束，这里N、M、K均为整数，且K≥1，M≥K，N＞M。其中，RSRP可以替换为其它的波束度量参数，比如RSRQ，SINR，BDRPM，波束角度等至少之一。
在一些示例中，基站或者终端可以从N个波束中选择M个波束，基站只发送M个波束对应的参考信号资源，终端通过接收所述M个波束对应的参考信号资源获得波束度量参数，并以一定的顺序组合成波束度量参数数组，对所述波 束度量参数数组进行归一化后输入神经网络，得到包括N个元素的波束度量参数数组，选择波束度量参数数组中波束度量参数(比如RSRP，SINR，RSRQ)最大的K个波束度量参数对应的波束为优选的波束。用至少一个优选的波束进行数据或者信号传输。这里波束包括发送波束和/或接收波束，这里N，M，K为整数，且K大于或等于1，M大于或等于K，N大于M。
在一些示例中，基站或者终端可以从N个波束中选择M个波束，基站只发送M个波束对应的参考信号资源，终端通过接收所述M个波束对应的参考信号资源获得波束度量参数，并以一定的顺序组合成波束度量参数数组，对所述波束度量参数数组进行归一化后输入神经网络，得到包括N个波束对应的概率，并选择其中概率最大的的K个波束为优选的波束。用至少一个优选的波束进行数据或者信号传输。这里波束包括发送波束和/或接收波束，这里N，M，K为整数，且K大于或等于1，M大于或等于K，N大于M。
在一些示例中，基站或者终端可以从N个波束中选择M个波束，基站只发送M个波束对应的参考信号资源，终端通过接收所述M个波束对应的参考信号资源获得波束度量参数，并以一定的顺序组合成波束度量参数数组，对所述波束度量参数数组进行归一化后输入神经网络，直接输出K个优选的波束或波束索引。用至少一个优选的波束进行数据或者信号传输。这里波束包括发送波束和/或接收波束，这里N，M，K为整数，且K大于或等于1，M大于或等于K，N大于M。
在一些示例中，基站或者终端可以从N个波束中选择M个波束，基站只发送M个波束对应的参考信号资源，终端通过接收所述M个波束对应的参考信号资源获得波束度量参数，并以一定的顺序组合成波束度量参数数组，反馈所述波束度量参数数组，基站接收所述波束度量参数数组，将所述波束度量参数数组进行归一化后输入神经网络，得到包括N个元素的波束度量参数数组，选择波束度量参数数组中波束度量参数(比如RSRP，SINR，RSRQ)最大的K个波束度量参数对应的波束为优选的波束。用至少一个优选的波束进行数据或者信号传输。这里波束包括发送波束和/或接收波束，这里N，M，K为整数，且K大于或等于1，M大于或等于K，N大于M。
在一些示例中，基站或者终端可以从N个波束中选择M个波束，基站只发送M个波束对应的参考信号资源，终端通过接收所述M个波束对应的参考信号资源获得波束度量参数，并以一定的顺序组合成波束度量参数数组，反馈所述波束度量参数数组，基站接收所述波束度量参数数组，将所述波束度量参数数组进行归一化后输入神经网络，得到包括N个波束对应的概率，并选择其中概率最大的的K个波束为优选的波束。用至少一个优选的波束进行数据或者信号 传输。这里波束包括发送波束和/或接收波束，这里N，M，K为整数，且K大于或等于1，M大于或等于K，N大于M。
在一些示例中，基站或者终端可以从N个波束中选择M个波束，基站只发送M个波束对应的参考信号资源，终端通过接收所述M个波束对应的参考信号资源获得波束度量参数，并以一定的顺序组合成波束度量参数数组，反馈所述波束度量参数数组，基站接收所述波束度量参数数组，将所述波束度量参数数组进行归一化后输入神经网络，直接输出K个优选的波束或波束索引。用至少一个优选的波束进行数据或者信号传输。这里波束包括发送波束和/或接收波束，这里N，M，K为整数，且K大于或等于1，M大于或等于K，N大于M。
在一些实施例中，所述N个波束对应D个参考信号资源，M个波束对应E个参考信号资源，基站发送所述E个参考信号资源，其中，E个参考信号资源属于D个参考信号资源的子集合，E个参考信号资源属于同一个参考信号资源集合。终端接收所述E个参考信号资源，并获得M个波束度量参数，并以一定的顺序组合成波束度量参数数组。在一个示例中，M＝E*R，R为实际采用的接收波束个数，N＝D*R1，其中R1为终端的全部波束个数，D为发送波束的个数。M，N，D，E，R，R1为正整数且N>M，D>E。
在这些实施例中，由于N>M，比如N是M的倍数，比如N是M的2、3、4、5、6等自然数。其中，N-M个波束是没有实际传输信号的，它的波束度量值是预测出来的，所以对于其中的一个发送波束，对应的最优的接收波束在接收端并不知道。有鉴于此，有必要提出一种可以确定预测的发送波束对应的接收波束的方案。
在一实施例中，图1是本申请实施例提供的一种通信方法的流程图。本实施例可以由发送端执行。其中，发送端可以为网络侧设备，比如基站等。如图1所示，本实施例中的通信方法包括：S110-S120。
S110、确定目标发送波束对应的目标接收波束。
目标发送波束为根据预设发送波束集合确定的发送波束，其中，预设发送波束集合包括第一发送波束集合和第二发送波束集合。其中，目标接收波束可以为目标发送波束对应的优选的接收波束。第一发送波束集合中的每个发送波束对应至少一个实际传输的参考信号资源，并且，第一发送波束集合中的至少一个发送波束在一定时刻进行了实际传输；第二发送波束集合对应零个参考信号资源，或者第二发送波束集合中的波束对应的参考信号资源并未进行实际传输。示例性地，假设第一发送波束集合为A＝【1、3、6、9、12、15】，第二发送波束集合为B＝【2、4、5、7、8、10、11、13、14】，并且，第一发送波束集合中的至少一个发送波束为实际进行传输的参考信号资源，第二发送波束集 合中的所有发送波束的度量参数均为预测的，并且第二发送波束集合中的波束对应未进行实际传输的参考信号资源，则预设发送波束集合为第一发送波束集合和第二发送波束集合的并集。
在实施例中，目标发送波束为根据预设发送波束集合确定的发送波束，目标发送波束为第一发送波束集合和/或第二发送波束集合中选择的一个或多个发送波束。在一实施例中，在目标发送波束为第二发送波束集合中的至少一个发送波束的情况下，由于第二发送波束集合中的所有发送波束未进行实际传输，需要根据一定的规则或方法确定目标发送波束对应的目标接收波束。
S120、发送目标接收波束指示信令，所述目标接收波束指示信令用于指示接收端确定目标接收波束，并用所述确定的目标接收波束进行信息传输。
信息传输包括：信息发送和信息接收。在实施例中，信息传输可以包括：数据传输和/或信号传输，即可以发送或接收数据，也可以发送或接收信号。在实施例中，在发送端确定目标发送波束对应的目标接收波束的情况下，发送端向接收端发送目标接收波束指示指令，所述目标接收波束指示信令用于指示接收端确定目标接收波束，并用所述确定的目标接收波束进行信息传输，从而在目标发送波束为未进行实际传输的参考信号资源的情况下，接收端仍可以根据发送端指示的与目标发送波束对应的目标接收波束进行信息传输。
在一实施例中，通过高层信令和/或物理层信令发送目标接收波束指示信令。在实施例中，发送端确定目标发送波束对应的目标接收波束之后，可以通过高层信令和/或物理层信令向接收端发送目标接收波束指示信令，所述目标接收波束指示信令用于指示接收端确定目标接收波束，并用所述确定的目标接收波束进行信息传输。
在一实施例中，目标发送波束为根据预设发送波束集合确定的发送波束，包括：根据约定的接收波束和预设发送波束集合对应的波束度量参数确定目标发送波束。在实施例中，可以根据约定的接收波束和预设发送波束集合中所有发送波束对应的波束度量参数确定目标发送波束。将预设发送波束中的每个发送波束与约定的接收波束组成一个波束对，得到对应的多个波束对(其中，波束对的数量与预设发送波束集合中发送波束的总数量相同)，然后每个波束对对应一个参考信号资源，传输所述参考信号资源，终端接收所述参考信号资源，测量所述终端接收的所述参考信号资源以得到每个波束对的波束度量参数。在一个示例中，终端根据波束度量参数确定优选波束对。在一个示例中，终端反馈所述波束度量参数，基站接收所述终端反馈的波束度量参数，基站根据波束度量参数确定优选波束对。示例性地，将波束度量参数最大的波束度量参数对应的波束对作为优选波束对，其中，所述优选波束对对应的发送波束为目标发 送波束，所述优选波束对对应的接收波束为目标接收波束。在实施例中，发送端可以采用目标发送波束传输数据或者信号，并且接收端采用目标接收波束接收数据或者信号。
在一实施例中，预设发送波束集合对应的波束度量参数根据约定的接收波束和第一发送波束集合对应的波束度量参数确定。其中，约定的接收波束指的是预先选择的一个固定的接收波束。在实施例中，预设发送波束集合指的是第一发送波束集合和第二发送波束集合的并集。第一发送波束集合对应的波束度量参数指的是第一发送波束集合中每个发送波束传输的参考信号和约定的接收波束组合成的多个波束对对应的波束度量参数，第一发送波束集合的每个波束传输与其对应的参考信号资源，终端通过约定的接收波束接收所述参考信号资源，测量所述终端接收的所述参考信号资源以得到每个波束对的波束度量参数。在一个示例中，终端将约定的接收波束和第一发送波束集合对应的波束度量参数输入神经网络以获得约定的接收波束和预设发送波束集组成的波束对对应的波束预测信息，并根据所述波束预测信息确定优选波束对。在一个示例中，终端反馈所述约定的接收波束和第一发送波束集合对应的波束度量参数，基站接收所述约定的接收波束和第一发送波束集合对应的波束度量参数，基站将所述约定的接收波束和第一发送波束集合对应的波束度量参数输入神经网络以获得约定的接收波束和预设发送波束集组成的波束对对应的波束预测信息，并根据所述波束预测信息确定优选波束对。这里波束预测信息可以是波束度量参数，也可以是波束对应的概论，或者是优选波束索引。示例性地，将波束度量参数最大的波束度量参数对应的波束对为优选波束对，其中，所述优选波束对对应的发送波束为目标发送波束，所述优选波束对对应的接收波束为目标接收波束。在实施例中，发送端可以采用目标发送波束传输数据或者信号，并且接收端采用目标接收波束接收数据或者信号。
在一实施例中，确定目标发送波束对应的目标接收波束，包括：确定约定的接收波束为目标接收波束，且约定的接收波束包括下述之一：接收波束索引最低的接收波束；接收波束索引最高的接收波束；预设接收波束集合中对应的波束度量参数最大的接收波束。在实施例中，发送端根据目标发送波束传输数据或者信号，并且，接收端采用约定的接收波束接收发送端传输的数据或信号。在一实施例中，约定的接收波束可以由网络侧(比如，基站)配置。在一示例中，约定的接收波束可以为约定的波束索引最低的接收波束；在一示例中，约定的接收波束可以为约定的波束索引最高的接收波束；在一示例中，约定的接收波束可以为波束度量参数最大的接收波束。一些实施例中，约定的接收波束是一个默认的波束或者终端自身确认或者基站根据信道情况确认指示终端确定的波束。
在一实施例中，确定目标发送波束对应的目标接收波束，包括：根据预设接收波束集合和约定的发送波束对应的波束度量参数确定目标接收波束。其中，约定的发送波束指的是预先选择的一个固定的发送波束。在实施例中，预设接收波束集合指的是第一接收波束集合和第二接收波束集合的并集。其中，第一接收波束集合包括至少在一个时刻接收了一个发送波束对应的参考信号资源的波束；第二接收波束集合中的所有接收波束均为未接收任何一个发送波束对应的参考信号资源。将预设接收波束集合中的每个接收波束与约定的发送波束组成一个波束对，进而得到对应的多个波束对(其中，波束对的数量与预设接收波束集合中接收波束的总数量相同)，约定的发送波束在多个时隙传输与其对应的参考信号资源，终端通过第一接收波束集合中的每个接收波束在多个时隙接收所述参考信号资源，测量所述终端接收所述的参考信号资源以得到每个波束对的波束度量参数。在一个示例中，终端将约定的发送波束和第一接收波束集合对应的波束度量参数输入神经网络以获得约定的发送波束和预设接收波束集合组成的波束对对应的波束预测信息，并根据所述波束预测信息确定优选波束对。在一个示例中，终端反馈所述约定的发送波束和第一接收波束集合对应的波束度量参数，基站接收所述约定的发送波束和第一接收波束集合对应的波束度量参数，基站将所述约定的发送波束和第一接收波束集合对应的波束度量参数输入神经网络以输出约定的发送波束和预设接收波束集合对应的波束预测信息，根据所述波束预测信息确定优选波束对。这里波束预测信息可以是波束度量参数，也可以是波束对应的概论，或者是优选波束索引。示例性地，将波束度量参数最大的波束度量参数对应的波束对作为优选波束对，其中，所述优选波束对对应的发送波束为目标发送波束，所述优选波束对对应的接收波束为目标接收波束。在实施例中，发送端可以采用目标发送波束传输数据或者信号，并且接收端采用目标接收波束接收数据或者信号。在一个示例中，第二接收波束集合为空，此时所述的预设接收波束集合等于第一接收波束集合。
在一实施例中，约定的发送波束包括下述之一：发送波束索引最低的发送波束；发送波束索引最高的发送波束；预设发送波束集中对应的波束度量参数最大的发送波束。在实施例中，约定的发送波束可以是网络侧(比如，基站)预先配置的。在一示例中，约定的发送波束可以为约定的波束索引最低的发送波束；在一示例中，约定的发送波束可以为约定的波束索引最高的发送波束；在一示例中，约定的发送波束可以为所有发送波束中波束度量参数最大的波束。在一些实施例中，约定的发送波束是一个全向天线对应的波束或者是一根全向天线。在一些实施例中，约定的发送波束是一个默认的波束或者基站自身确认或者终端根据信道情况确认指示基站确定的波束。
在一实施例中，预设接收波束集合包括第一接收波束集合和第二接收波束集合，且根据第一接收波束集合和约定的发送波束对应的波束度量参数确定预设接收波束集合对应的波束度量参数。利用第一接收波束集合对应的波束度量参数预测/确定第一接收波束集合和第二接收波束集合的并集(即预设接收波束集合)对应的波束度量参数。在实施例中，发送端可以采用约定的发送波束发送参考信号，并且，接收端采用第一接收波束集合中的接收波束接收约定的发送波束对应的参考信号，并将第一接收波束集合对应的波束度量参数作为神经网络的输入，并通过神经网络输出预设接收波束集合对应的波束度量参数。在一个示例中，终端反馈第一接收波束集合对应的波束度量参数，基站接收所述的第一接收波束集合对应的波束度量参数并将其作为神经网络的输入，并通过神经网络输出预设接收波束集合对应的波束度量参数。在一实施例中，根据所述预设接收波束集合对应的波束度量参数确定优选的波束对，并将优选波束对对应的发送波束确定为目标发送波束，将优选波束对对应的接收波束确定为目标接收波束。在一实施例中，预设接收波束集合的确定方式包括但不限于：人工智能的方式，插值方式。
在一实施例中，确定目标发送波束对应的目标接收波束，包括：根据预设接收波束集合和至少两个约定的发送波束对应的波束度量参数确定目标接收波束。在实施例中，可以通过多次测量的方式确定目标发送波束对应的目标接收波束。预先固定一个发送波束，并根据预设接收波束集合对应的波束度量参数和约定的发送波束确定一个优选接收波束；然后，再固定一个发送波束，并根据预设接收波束集合对应的波束度量参数和约定的发送波束确定一个优选接收波束；然后，再从两个优选接收波束中选择一个最终的目标接收波束。也可以采用上述方式确定三个或三个以上的优选接收波束，然后再从三个或三个以上的优选接收波束中确定一个最终的目标接收波束，以使接收端进行数据或信号的传输。
在一实施例中，应用于发送端的通信方法，还包括：
确定发送波束和接收波束的对应关系；根据对应关系和目标发送波束的波束索引确定目标接收波束。在实施例中，发送波束和接收波束的对应关系，指的是每个发送波束和接收波束之间的绑定关系或映射关系。在实施例中，可以在每个发送波束和接收波束之间建立对应关系，然后根据目标发送波束的波束索引和绑定关系查找对应接收波束的波束索引，并将该波束索引对应的接收波束作为目标接收波束。
在一实施例中，根据历史数据、全波束扫描的方法和人工智能的方法中的至少之一确定发送波束和接收波束的对应关系。其中，历史数据指的是对波束 进行扫描的历史数据。历史数据可以为对一个波束，也可以为对多个波束进行扫描的数据。在实施例中，全波束扫描的方法指的是对所有波束进行扫描的方法，比如根据全部波束扫描的方式得到不同发送波束和不同接收波束的波束度量参数，并选择波束度量参数最大的波束对为优选波束对，所述优选波束对即是发送波束和接收波束的对应关系；人工智能的方法指的是将部分波束对应的波束度量参数输入至神经网络中，以预测/确定所有波束对应的波束度量参数，并根据所述波束度量参数确定发送波束和接收波束的对应关系。比如根据波束度量参数中度量参数最大的波束对作为优选波束对，所述优选波束对即是发送波束和接收波束的对应关系。在实施例中，可以根据历史数据、全波束扫描的方法或人工智能的方法确定每个发送波束与接收波束之间的对应关系。
在一实施例中，发送波束和接收波束的对应关系包括下述之一：一个发送波束对应一个接收波束；一个发送波束和一个接收波束绑定；一个发送波束和一个接收波束关联；一个发送波束和一个接收波束配对。在实施例中，发送波束和接收波束之间的对应关系可以是一一对应关系，也可以是一对一绑定关系，也可以是一对一关联关系，也可以是一对一配对关系。
在一实施例中，应用于发送端的通信方法，还包括：
根据人工智能预测方式确定最优波束对；根据最优波束对确定目标发送波束对应的目标接收波束。其中，最优波束对指的是最优的发送波束和最优的接收波束的组合。在实施例中，网络侧或终端侧可以通过AI方式获得最优波束对，然后根据预测的最优发送波束(即目标发送波束)和最优波束对确定最优的接收波束，即确定目标接收波束；然后发送端采用最优发送波束发送数据或信号，以及接收端采用最优的接收波束接收数据或信号。
在一实施例中，确定目标发送波束对应的目标接收波束，包括：
采用重触发方式确定目标发送波束对应的目标接收波束。其中，重触发方式指的是重新触发的方式。发送端可以根据AI预测方式获取最优的发送波束，并采用该最优的发送波束向接收端传输参考信号资源，并触发接收端重新扫描接收波束，以使接收端确定最优的接收波束，并将该最优的接收波束保存在本地，以用于传输数据/信号。其中，最优的接收波束即为目标接收波束。
在一实施例中，应用于发送端的通信方法，还包括：
根据人工智能预测方式确定至少两个优选波束对或优选发送波束；根据所述至少两个优选波束对或者优选发送波束确定目标接收波束。
在实施例中，在根据人工智能预测方式确定至少两个优选波束对或优选发送波束的情况下，对至少两个优选波束对或优选发送波束进行测量，得到最优 接收波束；将最优接收波束作为目标发送波束对应的目标接收波束。在一实施例中，可以通过人工智能方式预测至少两个优选波束对，然后采用所有优选波束对中的最优发送波束发送参考信号资源至接收端，以使接收端在接收参考信号资源的过程中扫描接收波束，得到最优接收波束，并将最优接收波束作为目标接收波束。在一实施例中，可以通过人工智能方式直接预测至少两个优选发送波束，然后分别采用每个优选发送波束发送参考信号资源至接收端，以使接收端在接收参考信号资源的过程中扫描接收波束，得到最优接收波束，并将最优接收波束作为目标接收波束。示例性地，可以对所有波束对对应的波束度量参数进行排序，并将波束度量参数最大的K个波束度量参数对应的波束对作为优选波束对；或者，可以对所有发送波束对应的波束度量参数进行排序，并将波束度量参数最大的K个波束度量参数对应的发送波束作为优选发送波束。其中，K为正整数。
在一实施例中，根据波束度量参数对应的波束度量参数数组确定目标接收波束，其中，波束度量参数至少包括下述之一：约定的接收波束和第一发送波束集合对应的波束度量参数；第一接收波束集合和约定的发送波束所对应的波束度量参数，约定的接收波束和预设发送波束集合对应的波束度量参数；预设接收波束集合和约定的发送波束所对应的波束度量参数，第一接收波束集合和第一发送波束集合所对应的波束度量参数，第一接收波束集合和预设发送波束集合所对应的波束度量参数，预设接收波束集合和第一发送波束集合所对应的波束度量参数。在一实施例中，在将发送波束对应的波束度量参数输入至神经网络中的情况下，采用将约定的接收波束作为目标接收波束，以及根据预设发送波束集合对应的波束度量参数确定目标发送波束的方案；在一实施例中，在将接收波束对应的波束度量参数输入至神经网络中的情况下，采用将约定的发送波束作为目标发送波束，以及根据预设接收波束集合对应的波束度量参数和约定的发送波束确定目标接收波束的方案。在一实施例中，波束度量参数对应的数组，可以为发送波束和接收波束的波束度量参数组成的数组。示例性地，假设n1个发送波束，n2个接收波束，则波束度量参数对应的数组为由n1个发送波束的每个发送波束和n2个接收波束的每个接收波束对应的波束度量参数组成的二维数组。
在一实施例中，图2是本申请实施例提供的另一种通信方法的流程图。本实施例可以由接收端执行。其中，接收端可以为终端。如图2所示，本实施例中的通信方法包括：S210-S220。
S210、获取目标接收波束。
在一实施例中，可以通过接收发送端发送的目标接收波束指示指令获取目标接收波束；在一实施例中，第二通信节点根据测量的波束度量参数获取目标接收波束。
在一实施例中，波束度量参数至少包括下述之一：约定的接收波束和第一发送波束集合对应的波束度量参数；第一接收波束集合和约定的发送波束所对应的波束度量参数，约定的接收波束和预设发送波束集合对应的波束度量参数；预设接收波束集合和约定的发送波束所对应的波束度量参数，第一接收波束集合和第一发送波束集合所对应的波束度量参数，第一接收波束集合和预设发送波束集合所对应的波束度量参数，预设接收波束集合和第一发送波束集合所对应的波束度量参数。
S220、根据获取的目标接收波束进行信息传输。
目标接收波束与目标发送波束相对应，目标发送波束为根据预设发送波束集合确定的发送波束，其中，预设发送波束集合包括第一发送波束集合和第二发送波束集合。
在实施例中，对预设发送波束集合、目标接收波束、目标发送波束、第一发送波束集合和第二发送波束集合的解释，见上述应用于发送端的通信方法中对应参数的描述，在此不再赘述。
在实施例中，在接收端接收到发送端指示的目标接收波束之后，接收端通过目标接收波束进行信息传输。
在一实施例中，根据接收高层和/或者物理层传输的目标接收波束指示信令获取目标接收波束。
在一实施例中，目标发送波束对应的目标接收波束通过高层信令和/或物理层信令进行指示。
在一实施例中，目标发送波束为根据预设发送波束集合确定的发送波束，包括：根据约定的接收波束和预设发送波束集合对应的波束度量参数确定目标发送波束。
在一实施例中，预设发送波束集合对应的波束度量参数根据约定的接收波束和第一发送波束集合对应的波束度量参数确定。
在一实施例中，目标发送波束对应的目标接收波束的确定方式，包括：确定约定的接收波束为目标接收波束，且约定的接收波束包括下述之一：接收波束索引最低的接收波束；接收波束索引最高的接收波束；预设接收波束集合中 对应的波束度量参数最大的接收波束。
在一实施例中，目标发送波束对应的目标接收波束的确定方式，包括：根据预设接收波束集合和约定的发送波束对应的波束度量参数确定目标接收波束。
在一实施例中，约定的发送波束包括下述之一：发送波束索引最低的发送波束；发送波束索引最高的发送波束；预设发送波束集合中对应的波束度量参数最大的发送波束。
在一实施例中，预设接收波束集合包括第一接收波束集合和第二接收波束集合，且根据第一接收波束集合和约定的发送波束对应的波束度量参数确定预设接收波束集合对应的波束度量参数。
在一实施例中，目标发送波束对应的目标接收波束的确定方式，包括：根据预设接收波束集合和至少两个约定的发送波束对应的波束度量参数确定目标接收波束。
在一实施例中，应用于接收端的通信方法，还包括：
确定发送波束和接收波束的对应关系；根据对应关系和目标发送波束的波束索引确定目标接收波束。
在一实施例中，根据历史数据、全波束扫描的方法和人工智能的方法中的至少之一确定发送波束和接收波束的对应关系。
在一实施例中，发送波束和接收波束的对应关系包括下述之一：一个发送波束对应一个接收波束；一个发送波束和一个接收波束绑定；一个发送波束和一个接收波束关联；一个发送波束和一个接收波束配对。
在一实施例中，应用于接收端的通信方法，还包括：
根据人工智能预测方式确定最优波束对；根据最优波束对确定目标发送波束对应的目标接收波束。
在一实施例中，目标发送波束对应的目标接收波束的确定方式，包括：采用重触发方式确定目标发送波束对应的目标接收波束。
在一实施例中，应用于接收端的通信方法，还包括：
根据人工智能预测方式确定至少两个优选波束对或优选发送波束；根据所述至少两个优选波束对或者优选发送波束确定目标接收波束。
在一实施例中，根据波束度量参数对应的波束度量参数数组确定所述目标接收波束，其中，波束度量参数至少包括下述之一：约定的接收波束和第一发送波束集合对应的波束度量参数；第一接收波束集合和约定的发送波束所对应的波束度量参数，约定的接收波束和预设发送波束集合对应的波束度量参数； 预设接收波束集合和约定的发送波束所对应的波束度量参数，第一接收波束集合和第一发送波束集合所对应的波束度量参数，第一接收波束集合和预设发送波束集合所对应的波束度量参数，预设接收波束集合和第一发送波束集合所对应的波束度量参数。
上述应用于接收端的通信方法中各个参数的解释，可参见上述应用于发送端的通信方法的实施例中对应参数的描述，在此不再赘述。
在一个实施例中，可以选择一个约定的接收波束，并开启重复(repetition)参数为off，发送端用N1个发送波束发送参考信号资源，用约定的接收波束接收N1个发送波束对应的参考信号资源，并获得N1个发送波束对应的波束度量参数作为神经网络的输入，输出N个发送波束对应的波束度量参数。在一个示例中，接收端接反馈所述N1个发送波束对应的波束度量参数，基站接收所述N1个发送波束对应的波束度量参数并作为神经网络的输入，输出N个发送波束对应的波束度量参数。并选择N个发送波束对应的K个优选的波束作为最优波束。这里，N1，N，K为正整数，且，K<＝N1<N。那么发送端根据这K个优选波束中的至少一个波束(即目标发送波束)来传输数据或者信号，并且接收端用约定的接收波束(即目标接收波束)来接收传输的数据或者信号。在一个示例中，约定的接收波束是基站配置的，在一个示例中，约定的接收波束是约定的索引最低的波束，在一个示例中，约定的接收波束是约定的索引最高的波束，在一个示例中，约定的波束是波束度量参数最大的接收波束。
在一个实施例中，选择一个约定的发送波束，并开启重复(repetition)参数为on，发送端用选择的约定的发送波束发送参考信号资源，接收端用M1个接收波束接收发送波束对应的参考信号资源，并获得M1个接收波束对应的波束度量参数作为神经网络的输入，输出M个接收波束对应的波束度量参数，并选择M个接收波束对应的K个最优的波束作为优选波束。在一个示例中，接收端反馈所述M1个接收波束对应的波束度量参数，基站接收所述M1个接收波束对应的波束度量参数并作为神经网络的输入，输出M个接收波束对应的波束度量参数。并选择M个接收波束对应的K个最优的波束作为优选波束，并将所述K个优选波束通过物理层或者高层信令指示给接收端。这里，M1，M，K为正整数，且，K<＝M1<M。那么接收端根据这K个优选波束中的至少一个波束(即目标接收波束)来接收数据或者信号。在一个示例中，约定的发送波束(即目标发送波束)是基站配置的，在一个示例中，约定的发送波束是约定的索引最低的波束，在一个示例中，约定的发送波束是约定的索引最高的波束，在一个示例中，约定的发送波束是所有波束度量参数最大的发送波束。
在一个实施例中，选择一个约定的发送波束i，并开启重复(repetition)参数为on，发送端用选择的约定的发送波束发送参考信号资源，接收端用M1个接收波束接收发送波束对应的参考信号资源，并获得M1个接收波束对应的波束度量参数作为神经网络的输入，输出M个接收波束对应的波束度量值。并选择M个接收波束和所述第i个发送波束对应的1个最优的波束对作为最优波束对，记为Oi，这里，M1，M为正整数，且，M1<M，重复执行K次，i＝1，…，K。选择Oi中波束度量值最大的情况为最终的最优波束对，并发送端用最优的波束对对应的发送波束发送数据或者信号，接收端用最优的波束对对应的接收波束发送数据或者信号。同样的，在一个实施例中，终端需要将第i发送波束和所述M1个接收波束对应的波束度量参数反馈给基站，基站接收所述的波束度量参数，并输入神经网络以获得第i发送波束和所述M1个接收波束最优的波束对，重复执行K次以获得全局的最优的波束对，并将最优的波束对对应的接收波束通过高层信令或者物理层信令指示给接收端。基站用最优的波束对对应的发送波束传输数据或者信号，接收端用最优的波束对对应的接收波束传输数据或者信号。
在一个实施例中，每个发送波束和接收波束有对应的绑定关系，这种绑定关系可以根据历史数据，或者全空间的扫描波束获得，或者通过人工智能的预测方式学习得到。根据目标发送波束的波束索引和绑定关系确定接收端使用的目标接收波束。并发送端用最优的发送波束(即目标发送波束)对应的发送波束发送数据或者信号，接收端用最优的接收波束(即目标接收波束)对应的接收波束发送数据或者信号。
在一个实施例中，根据预测的方式获得最优的波束，基站或者终端通过AI预测的方式同时获得了最优的发送波束和接收波束对，根据预测的发送波束和接收波束对确定最优波束。并发送端用最优的波束对应的发送波束发送数据或者信号，接收端用最优的波束对应的接收波束发送数据或者信号。
在一个实施例中，发送端根据预测方式获得了最优的发送波束T0(即目标发送波束)，发送端采用发送波束T0发送参考信号，并开启repetition＝on，接收端接收参考信号，并进行波束扫描，选择波束度量参数最大的波束作为最优的接收波束(即目标接收波束)。终端在获得最优的接收波束后，保存本地用于接收。并跟发送波束T0建立QCL关系。并发送端用最优的发送波束对应的发送波束发送数据或者信号，接收端用最优的接收波束对应的接收波束发送数据或者信号。
在一个实施例中，发送端或者接收端预测K个优选的波束。在一个示例中，基站根据上报的波束度量参数预测K个优选波束对/或者优选的发送波束，并且，基于这K个波束发送参考信号资源进行测量得到K*M个波束度量参数，终端选 择K*M个中波束度量参数最大的的波束作为优选的波束对。并发送端用最优的波束对对应的发送波束发送数据或者信号，接收端用最优的波束对应的接收波束发送数据或者信号。这里，K为整数，M为接收波束的个数。
在一实施例中，图3是本申请实施例提供的一种通信装置的结构框图。本实施例应用于发送端。如图3所示，本实施例中的通信装置包括：第一确定模块310和发送模块320。
第一确定模块310，配置为确定目标发送波束对应的目标接收波束；其中，目标发送波束为根据预设发送波束集合确定的发送波束，其中，预设发送波束集合包括第一发送波束集合和第二发送波束集合。
发送模块320，配置为发送目标接收波束指示信令，所述目标接收波束指示信令用于指示接收端确定目标接收波束，并用所述确定的目标接收波束进行信息传输。
在一实施例中，通过高层信令和/或物理层信令发送所述目标接收波束指示信令。
在一实施例中，目标发送波束为根据预设发送波束集合确定的发送波束，包括：根据约定的接收波束和预设发送波束集合对应的波束度量参数确定目标发送波束。
在一实施例中，预设发送波束集合对应的波束度量参数根据约定的接收波束和第一发送波束集合对应的波束度量参数确定。
在一实施例中，第一确定模块310，包括：确定约定的接收波束为目标接收波束，且约定的接收波束包括下述之一：接收波束索引最低的接收波束；接收波束索引最高的接收波束；预设接收波束集合中对应的波束度量参数最大的接收波束。
在一实施例中，第一确定模块310，包括：根据预设接收波束集合和约定的发送波束对应的波束度量参数确定目标接收波束。
在一实施例中，约定的发送波束包括下述之一：发送波束索引最低的发送波束；发送波束索引最高的发送波束；预设发送波束集合中对应的波束度量参数最大的发送波束。
在一实施例中，预设接收波束集合包括第一接收波束集合和第二接收波束集合，且根据第一接收波束集合和约定的发送波束对应的波束度量参数确定预设接收波束集合对应的波束度量参数。
在一实施例中，第一确定模块310，包括：根据预设接收波束集合和至少两个约定的发送波束对应的波束度量参数确定目标接收波束。
在一实施例中，应用于发送端的通信装置，还包括：
第二确定模块，配置为确定发送波束和接收波束的对应关系；第三确定模块，配置为根据对应关系和目标发送波束的波束索引确定目标接收波束。
在一实施例中，根据历史数据、全波束扫描的方法和人工智能的方法中的至少之一确定发送波束和接收波束的对应关系。
在一实施例中，发送波束和接收波束的对应关系包括下述之一：一个发送波束对应一个接收波束；一个发送波束和一个接收波束绑定；一个发送波束和一个接收波束关联；一个发送波束和一个接收波束配对。
在一实施例中，应用于发送端的通信装置，还包括：
第四确定模块，配置为根据人工智能预测方式确定最优波束对；第五确定模块，配置为根据最优波束对确定目标发送波束对应的目标接收波束。
在一实施例中，第一确定模块310，包括：
采用重触发方式确定目标发送波束对应的目标接收波束。
在一实施例中，应用于发送端的通信装置，还包括：
第六确定模块，配置为根据人工智能预测方式确定至少两个优选波束对或优选发送波束；根据所述至少两个优选波束对或者优选发送波束确定目标接收波束。
在一实施例中，根据波束度量参数对应的波束度量参数数组确定目标接收波束，其中，波束度量参数至少包括下述之一：约定的接收波束和第一发送波束集合对应的波束度量参数；第一接收波束集合和约定的发送波束所对应的波束度量参数，约定的接收波束和预设发送波束集合对应的波束度量参数；预设接收波束集合和约定的发送波束所对应的波束度量参数，第一接收波束集合和第一发送波束集合所对应的波束度量参数，第一接收波束集合和预设发送波束集合所对应的波束度量参数，预设接收波束集合和第一发送波束集合所对应的波束度量参数。
本实施例提供的通信装置设置为实现图1所示实施例的应用于发送端的通信方法，本实施例提供的通信装置实现原理和技术效果类似，此处不再赘述。
在一实施例中，图4是本申请实施例提供的另一种通信装置的结构框图。本实施例应用于接收端。如图4所示，本实施例中的通信装置包括：获取模块 410和传输模块420。
获取模块410，配置为获取目标接收波束；传输模块420，配置为根据获取的目标接收波束进行信息传输；目标接收波束与目标发送波束相对应，目标发送波束为根据预设发送波束集合确定的发送波束，其中，预设发送波束集合包括第一发送波束集合和第二发送波束集合。
在一实施例中，根据接收高层和/或者物理层传输的目标接收波束指示信令获取目标接收波束。
在一实施例中，目标发送波束为根据预设发送波束集合确定的发送波束，包括：根据约定的接收波束和预设发送波束集合对应的波束度量参数确定目标发送波束。
在一实施例中，预设发送波束集合对应的波束度量参数根据约定的接收波束和第一发送波束集合对应的波束度量参数确定。
在一实施例中，目标发送波束对应的目标接收波束的确定方式，包括：确定约定的接收波束为目标接收波束，且约定的接收波束包括下述之一：接收波束索引最低的接收波束；接收波束索引最高的接收波束；预设接收波束集合中对应的波束度量参数最大的接收波束。
在一实施例中，目标发送波束对应的目标接收波束的确定方式，包括：根据预设接收波束集合和约定的发送波束对应的波束度量参数确定目标接收波束。
在一实施例中，约定的发送波束包括下述之一：发送波束索引最低的发送波束；发送波束索引最高的发送波束；预设发送波束集合中对应的波束度量参数最大的发送波束。
在一实施例中，预设接收波束集合包括第一接收波束集合和第二接收波束集合，且根据第一接收波束集合和约定的发送波束对应的波束度量参数确定预设接收波束集合对应的波束度量参数。
在一实施例中，目标发送波束对应的目标接收波束的确定方式，包括：根据预设接收波束集合和至少两个约定的发送波束对应的波束度量参数确定目标接收波束。
在一实施例中，应用于接收端的通信装置，还包括：
第一确定模块，配置为确定发送波束和接收波束的对应关系；第二确定模块，配置为根据对应关系和目标发送波束的波束索引确定目标接收波束。
在一实施例中，根据历史数据、全波束扫描的方法和人工智能的方法中的至少之一确定发送波束和接收波束的对应关系。
在一实施例中，发送波束和接收波束的对应关系包括下述之一：一个发送波束对应一个接收波束；一个发送波束和一个接收波束绑定；一个发送波束和一个接收波束关联；一个发送波束和一个接收波束配对。
在一实施例中，应用于接收端的通信装置，还包括：
第三确定模块，配置为根据人工智能预测方式确定最优波束对；第四确定模块，配置为根据最优波束对确定目标发送波束对应的目标接收波束。
在一实施例中，目标发送波束对应的目标接收波束的确定方式，包括：采用重触发方式确定目标发送波束对应的目标接收波束。
在一实施例中，应用于接收端的通信装置，还包括：
第五确定模块，配置为根据人工智能预测方式确定至少两个优选波束对或优选发送波束；第六确定模块，配置为根据所述至少两个优选波束对或者优选发送波束确定目标接收波束。
在一实施例中，根据波束度量参数对应的波束度量参数数组确定所述目标接收波束，其中，波束度量参数至少包括下述之一：约定的接收波束和第一发送波束集合对应的波束度量参数；第一接收波束集合和约定的发送波束所对应的波束度量参数，约定的接收波束和预设发送波束集合对应的波束度量参数；预设接收波束集合和约定的发送波束所对应的波束度量参数，第一接收波束集合和第一发送波束集合所对应的波束度量参数，第一接收波束集合和预设发送波束集合所对应的波束度量参数，预设接收波束集合和第一发送波束集合所对应的波束度量参数。
本实施例提供的通信装置设置为实现图2所示实施例的应用于接收端的通信方法，本实施例提供的通信装置实现原理和技术效果类似，此处不再赘述。
在一实施例中，图5是本申请实施例提供的一种通信设备的结构示意图。如图5所示，本申请提供的设备，包括：处理器510和存储器520。该设备中处理器510的数量可以是一个或者多个，图5中以一个处理器510为例。该设备中存储器520的数量可以是一个或者多个，图5中以一个存储器520为例。该设备的处理器510和存储器520可以通过总线或者其他方式连接，图5中以通过总线连接为例。在该实施例中，通信设备可以作为发送端。
存储器520作为一种计算机可读存储介质，可设置为存储软件程序、计算机可执行程序以及模块，如本申请任意实施例的设备对应的程序指令/模块(例如，应用于发送端的通信装置中的第一确定模块310和发送模块320)。存储器520可包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少 一个功能所需的应用程序；存储数据区可存储根据设备的使用所创建的数据等。此外，存储器520可以包括高速随机存取存储器，还可以包括非易失性存储器，例如至少一个磁盘存储器件、闪存器件、或其他非易失性固态存储器件。在一些实例中，存储器520可进一步包括相对于处理器510远程设置的存储器，这些远程存储器可以通过网络连接至设备。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。
在通信设备为发送端的情况下，上述提供的设备可设置为执行上述任意实施例提供的应用于发送端的通信方法，具备相应的功能和效果。
在通信设备为接收端的情况下，上述提供的设备可设置为执行上述任意实施例提供的应用于接收端的通信方法，具备相应的功能和效果。
本申请实施例还提供一种包含计算机可执行指令的存储介质，计算机可执行指令在由计算机处理器执行时用于执行一种应用于发送端的通信方法，该方法包括：确定目标发送波束对应的目标接收波束；其中，目标发送波束为根据预设发送波束集合确定的发送波束，其中，预设发送波束集合包括第一发送波束集合和第二发送波束集合；发送目标接收波束指示信令，所述目标接收波束指示信令用于指示接收端确定目标接收波束，并用所述确定的目标接收波束进行信息传输。
本申请实施例还提供一种包含计算机可执行指令的存储介质，计算机可执行指令在由计算机处理器执行时用于执行一种应用于接收端的通信方法，该方法包括：获取目标接收波束；根据获取的目标接收波束进行信息传输；目标接收波束与目标发送波束相对应，目标发送波束为根据预设发送波束集合确定的发送波束，其中，预设发送波束集合包括第一发送波束集合和第二发送波束集合。
本领域内的技术人员应明白，术语用户设备涵盖任何适合类型的无线用户设备，例如移动电话、便携数据处理装置、便携网络浏览器或车载移动台。
一般来说，本申请的多种实施例可以在硬件或专用电路、软件、逻辑或其任何组合中实现。例如，一些方面可以被实现在硬件中，而其它方面可以被实现在可以被控制器、微处理器或其它计算装置执行的固件或软件中，尽管本申请不限于此。
本申请的实施例可以通过移动装置的数据处理器执行计算机程序指令来实现，例如在处理器实体中，或者通过硬件，或者通过软件和硬件的组合。计算机程序指令可以是汇编指令、指令集架构(Instruction Set Architecture，ISA)指令、机器指令、机器相关指令、微代码、固件指令、状态设置数据、或者以一 种或多种编程语言的任意组合编写的源代码或目标代码。
本申请附图中的任何逻辑流程的框图可以表示程序步骤，或者可以表示相互连接的逻辑电路、模块和功能，或者可以表示程序步骤与逻辑电路、模块和功能的组合。计算机程序可以存储在存储器上。存储器可以具有任何适合于本地技术环境的类型并且可以使用任何适合的数据存储技术实现，例如但不限于只读存储器(Read-Only Memory，ROM)、随机访问存储器(Random Access Memory，RAM)、光存储器装置和系统(数码多功能光碟(Digital Video Disc，DVD)或光盘(Compact Disk，CD))等。计算机可读介质可以包括非瞬时性存储介质。数据处理器可以是任何适合于本地技术环境的类型，例如但不限于通用计算机、专用计算机、微处理器、数字信号处理器(Digital Signal Processing，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、可编程逻辑器件(Field-Programmable Gate Array，FPGA)以及基于多核处理器架构的处理器。

Claims (20)

  1. 一种通信方法，应用于发送端，包括：
    确定目标发送波束对应的目标接收波束；其中，所述目标发送波束为根据预设发送波束集合确定的发送波束，其中，所述预设发送波束集合包括第一发送波束集合和第二发送波束集合；
    发送目标接收波束指示信令，所述目标接收波束指示信令用于指示接收端确定所述目标接收波束，并用确定的所述目标接收波束进行信息传输。
  2. 根据权利要求1所述的方法，其中，所述发送目标接收波束指示信令，包括：
    通过高层信令和物理层信令中的至少之一发送所述目标接收波束指示信令。
  3. 根据权利要求1所述的方法，其中，所述目标发送波束为根据预设发送波束集合确定的发送波束，包括：
    根据约定的接收波束和预设发送波束集合对应的波束度量参数确定所述目标发送波束。
  4. 根据权利要求3所述的方法，其中，所述约定的接收波束和预设发送波束集合对应的波束度量参数根据约定的接收波束和第一发送波束集合对应的波束度量参数确定。
  5. 根据权利要求4所述的方法，其中，所述确定目标发送波束对应的目标接收波束，包括：
    确定所述约定的接收波束为目标接收波束，且所述约定的接收波束包括下述之一：接收波束索引最低的接收波束；接收波束索引最高的接收波束；预设接收波束集合中对应的波束度量参数最大的接收波束。
  6. 根据权利要求1所述的方法，其中，所述确定目标发送波束对应的目标接收波束，包括：
    根据预设接收波束集合和约定的发送波束对应的波束度量参数确定所述目标接收波束。
  7. 根据权利要求6所述的方法，其中，所述约定的发送波束包括下述之一：发送波束索引最低的发送波束；发送波束索引最高的发送波束；预设发送波束集合中对应的波束度量参数最大的发送波束。
  8. 根据权利要求6所述的方法，其中，所述预设接收波束集合包括第一接收波束集合和第二接收波束集合，且根据第一接收波束集合和约定的发送波束对应的波束度量参数确定所述预设接收波束集合和约定的发送波束对应的波束度 量参数。
  9. 根据权利要求1所述的方法，其中，所述确定目标发送波束对应的目标接收波束，包括：
    根据预设接收波束集合和至少两个约定的发送波束对应的波束度量参数确定所述目标接收波束。
  10. 根据权利要求1所述的方法，所述确定目标发送波束对应的目标接收波束，还包括：
    确定发送波束和接收波束的对应关系；
    根据所述对应关系和所述目标发送波束的波束索引确定所述目标接收波束。
  11. 根据权利要求10所述的方法，其中，所述确定发送波束和接收波束的对应关系，包括：
    根据历史数据、全波束扫描的方法和人工智能的方法中的至少之一确定所述发送波束和接收波束的对应关系。
  12. 根据权利要求10所述的方法，其中，所述发送波束和接收波束的对应关系包括下述之一：一个发送波束对应一个接收波束；一个发送波束和一个接收波束绑定；一个发送波束和一个接收波束关联；一个发送波束和一个接收波束配对。
  13. 根据权利要求1所述的方法，所述确定目标发送波束对应的目标接收波束，还包括：
    根据人工智能预测方式确定最优波束对；
    根据所述最优波束对确定所述目标发送波束对应的目标接收波束。
  14. 根据权利要求1所述的方法，其中，所述确定目标发送波束对应的目标接收波束，包括：
    采用重触发方式确定所述目标发送波束对应的目标接收波束。
  15. 根据权利要求1所述的方法，还包括：
    根据人工智能预测方式确定至少两个优选波束对或优选发送波束；
    根据所述至少两个优选波束对或者优选发送波束确定所述目标接收波束。
  16. 根据权利要求1所述的方法，其中，根据波束度量参数对应的波束度量参数数组确定所述目标接收波束，其中，波束度量参数至少包括下述之一：约定的接收波束和第一发送波束集合对应的波束度量参数；第一接收波束集合和 约定的发送波束所对应的波束度量参数，约定的接收波束和预设发送波束集合对应的波束度量参数；预设接收波束集合和约定的发送波束所对应的波束度量参数，第一接收波束集合和第一发送波束集合所对应的波束度量参数，第一接收波束集合和预设发送波束集合所对应的波束度量参数，预设接收波束集合和第一发送波束集合所对应的波束度量参数。
  17. 一种通信方法，应用于接收端，包括：
    获取目标接收波束；
    根据获取的所述目标接收波束进行信息传输；所述目标接收波束与目标发送波束相对应，所述目标发送波束为根据预设发送波束集合确定的发送波束，其中，所述预设发送波束集合包括第一发送波束集合和第二发送波束集合。
  18. 根据权利要求17所述的方法，其中，所述获取目标接收波束，包括：
    根据接收高层和物理层中至少之一传输的目标接收波束指示信令获取所述目标接收波束。
  19. 一种通信设备，包括：存储器，以及至少一个处理器；
    所述存储器，配置为存储至少一个程序；
    当所述至少一个程序被所述至少一个处理器执行，使得所述至少一个处理器实现如上述权利要求1-16或17-18中任一项所述的通信方法。
  20. 一种存储介质，所述存储介质存储有计算机程序，所述计算机程序被处理器执行时实现如上述权利要求1-16或17-18中任一项所述的通信方法。
PCT/CN2023/089989 2022-04-29 2023-04-23 通信方法、设备和存储介质 WO2023207822A1 (zh)

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CN109392123A (zh) * 2017-08-10 2019-02-26 株式会社Ntt都科摩 波束选择方法、基站和用户设备
CN111543012A (zh) * 2017-12-15 2020-08-14 高通股份有限公司 用于动态波束对确定的方法和设备
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CN112910528A (zh) * 2017-02-13 2021-06-04 Oppo广东移动通信有限公司 无线通信方法、终端设备和网络设备
CN109392123A (zh) * 2017-08-10 2019-02-26 株式会社Ntt都科摩 波束选择方法、基站和用户设备
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